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Author's title

Paper 2012, Deel 3. ANOVA & Meervoudige Regressie; one-way ANOVA simple lin...

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 16 Dec 2012 15:18:31 -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/2012/Dec/16/t1355689148jlkkilay8o12jr1.htm/, Retrieved Fri, 29 Mar 2024 10:10:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200574, Retrieved Fri, 29 Mar 2024 10:10:16 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper 2012, Deel ...] [2012-12-16 20:18:31] [e4c351aee2a0bb2c047702ea90f356fa] [Current]
- RMP     [Simple Linear Regression] [Paper 2012, Deel ...] [2012-12-16 20:43:59] [346d881f0ddec1e86442b342eb3e8104]
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Dataseries X:
14	12
18	11
11	14
12	12
16	21
18	12
14	22
14	11
15	10
15	13
17	10
19	8
10	15
16	14
18	10
14	14
14	14
17	11
14	10
16	13
18	7
11	14
14	12
12	14
17	11
9	9
16	11
14	15
15	14
11	13
16	9
13	15
17	10
15	11
14	13
16	8
9	20
15	12
17	10
13	10
15	9
16	14
16	8
12	14
12	11
11	13
15	9
15	11
17	15
13	11
16	10
14	14
11	18
12	14
12	11
15	12
16	13
15	9
12	10
12	15
8	20
13	12
11	12
14	14
15	13
10	11
11	17
12	12
15	13
15	14
14	13
16	15
15	13
15	10
13	11
12	19
17	13
13	17
15	13
13	9
15	11
16	10
15	9
16	12
15	12
14	13
15	13
14	12
13	15
7	22
17	13
13	15
15	13
14	15
13	10
16	11
12	16
14	11
17	11
15	10
17	10
12	16
16	12
11	11
15	16
9	19
16	11
15	16
10	15
10	24
15	14
11	15
13	11
14	15
18	12
16	10
14	14
14	13
14	9
14	15
12	15
14	14
15	11
15	8
15	11
13	11
17	8
17	10
19	11
15	13
13	11
9	20
15	10
15	15
15	12
16	14
11	23
14	14
11	16
15	11
13	12
15	10
16	14
14	12
15	12
16	11
16	12
11	13
12	11
9	19
16	12
13	17
16	9
12	12
9	19
13	18
13	15
14	14
19	11
13	9
12	18
13	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200574&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]11 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=200574&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200574&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 19.3158785241537 -0.398870553790956Depression[t] -0.00154027507600006t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  19.3158785241537 -0.398870553790956Depression[t] -0.00154027507600006t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200574&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  19.3158785241537 -0.398870553790956Depression[t] -0.00154027507600006t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200574&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200574&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 19.3158785241537 -0.398870553790956Depression[t] -0.00154027507600006t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19.31587852415370.67985628.411700
Depression-0.3988705537909560.049503-8.057500
t-0.001540275076000060.003341-0.4610.6454350.322717

\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) & 19.3158785241537 & 0.679856 & 28.4117 & 0 & 0 \tabularnewline
Depression & -0.398870553790956 & 0.049503 & -8.0575 & 0 & 0 \tabularnewline
t & -0.00154027507600006 & 0.003341 & -0.461 & 0.645435 & 0.322717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200574&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]19.3158785241537[/C][C]0.679856[/C][C]28.4117[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Depression[/C][C]-0.398870553790956[/C][C]0.049503[/C][C]-8.0575[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.00154027507600006[/C][C]0.003341[/C][C]-0.461[/C][C]0.645435[/C][C]0.322717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200574&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200574&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19.31587852415370.67985628.411700
Depression-0.3988705537909560.049503-8.057500
t-0.001540275076000060.003341-0.4610.6454350.322717







Multiple Linear Regression - Regression Statistics
Multiple R0.545101721019329
R-squared0.297135886258234
Adjusted R-squared0.288294828223746
F-TEST (value)33.6086342945779
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value6.7035266226867e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.97207585944176
Sum Squared Residuals618.364228067478

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.545101721019329 \tabularnewline
R-squared & 0.297135886258234 \tabularnewline
Adjusted R-squared & 0.288294828223746 \tabularnewline
F-TEST (value) & 33.6086342945779 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 6.7035266226867e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.97207585944176 \tabularnewline
Sum Squared Residuals & 618.364228067478 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200574&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.545101721019329[/C][/ROW]
[ROW][C]R-squared[/C][C]0.297135886258234[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.288294828223746[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]33.6086342945779[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C]6.7035266226867e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.97207585944176[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]618.364228067478[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200574&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200574&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.545101721019329
R-squared0.297135886258234
Adjusted R-squared0.288294828223746
F-TEST (value)33.6086342945779
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value6.7035266226867e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.97207585944176
Sum Squared Residuals618.364228067478







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.5278916035863-0.527891603586283
21814.92522188230123.07477811769882
31113.7270699458523-2.72706994585232
41214.5232707783582-2.52327077835823
51610.93189551916365.06810448083637
61814.52019022820623.47980977179377
71410.52994441522073.47005558477933
81414.9159802318452-0.915980231845184
91515.3133105105601-0.31331051056014
101514.11515857411130.884841425888727
111715.31022996040811.68977003959186
121916.10643079291412.89356920708595
131013.3127966413014-3.31279664130136
141613.71012692001632.28987307998368
151815.30406886010412.69593113989586
161413.70704636986430.292953630135683
171413.70550609478830.294493905211683
181714.90057748108522.09942251891482
191415.2979077598001-1.29790775980014
201614.09975582335131.90024417664873
211816.4914388710211.50856112897899
221113.6978047194083-2.69780471940832
231414.4940055519142-0.494005551914228
241213.6947241692563-1.69472416925632
251714.88979555555322.11020444444682
26915.6859963880591-6.68599638805909
271614.88671500540121.11328499459882
281413.28969251516140.710307484838639
291513.68702279387631.31297720612368
301114.0843530725913-3.08435307259127
311615.67829501267910.321704987320906
321313.2835314148574-0.28353141485736
331715.27634390873611.72365609126386
341514.87593307986920.124066920130817
351414.0766516972113-0.0766516972112713
361616.06946419109-0.069464191090049
37911.2814772705226-2.28147727052258
381514.47090142577420.529098574225773
391715.26710225828011.73289774171986
401315.2655619832041-2.26556198320414
411515.6628922619191-0.662892261919093
421613.66699921788832.33300078211168
431616.058682265558-0.0586822655580486
441213.6639186677363-1.66391866773632
451214.8589900540332-2.85899005403318
461114.0597086713753-3.05970867137527
471515.6536506114631-0.653650611463093
481514.85436922880520.145630771194818
491713.25734673856543.74265326143464
501314.8512886786532-1.85128867865318
511615.24861895736810.751381042631863
521413.65159646712830.348403532871685
531112.0545739768885-1.05457397688849
541213.6485159169763-1.64851591697631
551214.8435873032732-2.84358730327318
561514.44317647440620.556823525593774
571614.04276564553931.95723435446073
581515.6367075856271-0.636707585627092
591215.2362967567601-3.23629675676014
601213.2404037127294-1.24040371272936
61811.2445106686986-3.24451066869858
621314.4339348239502-1.43393482395023
631114.4323945488742-3.43239454887423
641413.63311316621630.366886833783686
651514.03044344493130.969556555068731
661014.8266442774372-4.82664427743718
671112.4318806796154-1.43188067961545
681214.4246931734942-2.42469317349422
691514.02428234462730.975717655372731
701513.62387151576031.37612848423969
711414.0212017944753-0.0212017944752693
721613.22192041181742.77807958818264
731514.01812124432330.981878755676731
741515.2131926306201-0.213192630620136
751314.8127818017532-1.81278180175318
761211.62027709634950.379722903650464
771714.01196014401932.98803985598073
781312.41493765377940.585062346220553
791514.00887959386730.991120406132731
801315.6028215339551-2.60282153395509
811514.80354015129720.19645984870282
821615.20087043001210.799129569987865
831515.5982007087271-0.598200708727091
841614.40004877227821.59995122772178
851514.39850849720220.601491502797776
861413.99809766833530.00190233166473155
871513.99655739325931.00344260674073
881414.3938876719742-0.393887671974224
891313.1957357355254-0.195735735525357
90710.4021015839127-3.40210158391267
911713.99039629295533.00960370704473
921313.1911149102974-0.191114910297357
931513.98731574280331.01268425719673
941413.18803436014540.811965639854643
951315.1808468540241-2.18084685402413
961614.78043602515721.21956397484282
971212.7845429811264-0.784542981126401
981414.7773554750052-0.777355475005179
991714.77581519992922.22418480007082
1001515.1731454786441-0.173145478644134
1011715.17160520356811.82839479643187
1021212.7768416057464-0.7768416057464
1031614.37078354583421.62921645416578
1041114.7681138245492-3.76811382454918
1051512.77222078051842.2277792194816
106911.5740688440695-2.57406884406953
1071614.76349299932121.23650700067882
1081512.76759995529042.2324000447096
1091013.1649302340054-3.16493023400536
110109.573554974810760.426445025189244
1111513.56072023764431.43927976235569
1121113.1603094087774-2.16030940877736
1131314.7542513488652-1.75425134886518
1141413.15722885862540.842771141374644
1151814.35230024492223.64769975507778
1161615.14850107742810.851498922571867
1171413.55147858718830.448521412811689
1181413.94880886590330.0511911340967334
1191415.5427508059911-1.54275080599109
1201413.14798720816940.852012791830645
1211213.1464469330934-1.14644693309336
1221413.54377721180830.456222788191689
1231514.73884859810520.261151401894822
1241515.933919984402-0.933919984402044
1251514.73576804795320.264231952046823
1261314.7342277728772-1.73422777287718
1271715.9292991591741.07070084082596
1281715.13001777651611.86998222348387
1291914.72960694764924.27039305235082
1301513.93032556499131.06967443500873
1311314.7265263974972-1.72652639749718
132911.1351511383026-2.13515113830258
1331515.1223164011361-0.122316401136132
1341513.12642335710541.87357664289465
1351514.32149474340220.678505256597779
1361613.52221336074432.47778663925569
137119.930838101549711.06916189845029
1381413.51913281059230.48086718940769
1391112.7198514279344-1.7198514279344
1401514.71266392181320.287336078186823
1411314.3122530929462-1.31225309294622
1421515.1084539254521-0.108453925452132
1431613.51143143521232.48856856478769
1441414.3076322677182-0.307632267718221
1451514.30609199264220.693908007357779
1461614.70342227135721.29657772864282
1471614.30301144249021.69698855750978
1481113.9026006136233-2.90260061362326
1491214.6988014461292-2.69880144612918
150911.5062967407255-2.50629674072553
1511614.29685034218621.70314965781378
1521312.30095729815540.699042701844558
1531615.49038145340710.509618546592913
1541214.2922295169582-2.29222951695822
155911.4985953653455-2.49859536534553
1561311.89592564406051.10407435593951
1571313.0909970303574-0.0909970303573532
1581413.48832730907230.511672690927691
1591914.68339869536924.31660130463082
1601315.4795995278751-2.47959952787509
1611211.88822426868050.111775731319514
1621312.68442510118640.315574898813603

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.5278916035863 & -0.527891603586283 \tabularnewline
2 & 18 & 14.9252218823012 & 3.07477811769882 \tabularnewline
3 & 11 & 13.7270699458523 & -2.72706994585232 \tabularnewline
4 & 12 & 14.5232707783582 & -2.52327077835823 \tabularnewline
5 & 16 & 10.9318955191636 & 5.06810448083637 \tabularnewline
6 & 18 & 14.5201902282062 & 3.47980977179377 \tabularnewline
7 & 14 & 10.5299444152207 & 3.47005558477933 \tabularnewline
8 & 14 & 14.9159802318452 & -0.915980231845184 \tabularnewline
9 & 15 & 15.3133105105601 & -0.31331051056014 \tabularnewline
10 & 15 & 14.1151585741113 & 0.884841425888727 \tabularnewline
11 & 17 & 15.3102299604081 & 1.68977003959186 \tabularnewline
12 & 19 & 16.1064307929141 & 2.89356920708595 \tabularnewline
13 & 10 & 13.3127966413014 & -3.31279664130136 \tabularnewline
14 & 16 & 13.7101269200163 & 2.28987307998368 \tabularnewline
15 & 18 & 15.3040688601041 & 2.69593113989586 \tabularnewline
16 & 14 & 13.7070463698643 & 0.292953630135683 \tabularnewline
17 & 14 & 13.7055060947883 & 0.294493905211683 \tabularnewline
18 & 17 & 14.9005774810852 & 2.09942251891482 \tabularnewline
19 & 14 & 15.2979077598001 & -1.29790775980014 \tabularnewline
20 & 16 & 14.0997558233513 & 1.90024417664873 \tabularnewline
21 & 18 & 16.491438871021 & 1.50856112897899 \tabularnewline
22 & 11 & 13.6978047194083 & -2.69780471940832 \tabularnewline
23 & 14 & 14.4940055519142 & -0.494005551914228 \tabularnewline
24 & 12 & 13.6947241692563 & -1.69472416925632 \tabularnewline
25 & 17 & 14.8897955555532 & 2.11020444444682 \tabularnewline
26 & 9 & 15.6859963880591 & -6.68599638805909 \tabularnewline
27 & 16 & 14.8867150054012 & 1.11328499459882 \tabularnewline
28 & 14 & 13.2896925151614 & 0.710307484838639 \tabularnewline
29 & 15 & 13.6870227938763 & 1.31297720612368 \tabularnewline
30 & 11 & 14.0843530725913 & -3.08435307259127 \tabularnewline
31 & 16 & 15.6782950126791 & 0.321704987320906 \tabularnewline
32 & 13 & 13.2835314148574 & -0.28353141485736 \tabularnewline
33 & 17 & 15.2763439087361 & 1.72365609126386 \tabularnewline
34 & 15 & 14.8759330798692 & 0.124066920130817 \tabularnewline
35 & 14 & 14.0766516972113 & -0.0766516972112713 \tabularnewline
36 & 16 & 16.06946419109 & -0.069464191090049 \tabularnewline
37 & 9 & 11.2814772705226 & -2.28147727052258 \tabularnewline
38 & 15 & 14.4709014257742 & 0.529098574225773 \tabularnewline
39 & 17 & 15.2671022582801 & 1.73289774171986 \tabularnewline
40 & 13 & 15.2655619832041 & -2.26556198320414 \tabularnewline
41 & 15 & 15.6628922619191 & -0.662892261919093 \tabularnewline
42 & 16 & 13.6669992178883 & 2.33300078211168 \tabularnewline
43 & 16 & 16.058682265558 & -0.0586822655580486 \tabularnewline
44 & 12 & 13.6639186677363 & -1.66391866773632 \tabularnewline
45 & 12 & 14.8589900540332 & -2.85899005403318 \tabularnewline
46 & 11 & 14.0597086713753 & -3.05970867137527 \tabularnewline
47 & 15 & 15.6536506114631 & -0.653650611463093 \tabularnewline
48 & 15 & 14.8543692288052 & 0.145630771194818 \tabularnewline
49 & 17 & 13.2573467385654 & 3.74265326143464 \tabularnewline
50 & 13 & 14.8512886786532 & -1.85128867865318 \tabularnewline
51 & 16 & 15.2486189573681 & 0.751381042631863 \tabularnewline
52 & 14 & 13.6515964671283 & 0.348403532871685 \tabularnewline
53 & 11 & 12.0545739768885 & -1.05457397688849 \tabularnewline
54 & 12 & 13.6485159169763 & -1.64851591697631 \tabularnewline
55 & 12 & 14.8435873032732 & -2.84358730327318 \tabularnewline
56 & 15 & 14.4431764744062 & 0.556823525593774 \tabularnewline
57 & 16 & 14.0427656455393 & 1.95723435446073 \tabularnewline
58 & 15 & 15.6367075856271 & -0.636707585627092 \tabularnewline
59 & 12 & 15.2362967567601 & -3.23629675676014 \tabularnewline
60 & 12 & 13.2404037127294 & -1.24040371272936 \tabularnewline
61 & 8 & 11.2445106686986 & -3.24451066869858 \tabularnewline
62 & 13 & 14.4339348239502 & -1.43393482395023 \tabularnewline
63 & 11 & 14.4323945488742 & -3.43239454887423 \tabularnewline
64 & 14 & 13.6331131662163 & 0.366886833783686 \tabularnewline
65 & 15 & 14.0304434449313 & 0.969556555068731 \tabularnewline
66 & 10 & 14.8266442774372 & -4.82664427743718 \tabularnewline
67 & 11 & 12.4318806796154 & -1.43188067961545 \tabularnewline
68 & 12 & 14.4246931734942 & -2.42469317349422 \tabularnewline
69 & 15 & 14.0242823446273 & 0.975717655372731 \tabularnewline
70 & 15 & 13.6238715157603 & 1.37612848423969 \tabularnewline
71 & 14 & 14.0212017944753 & -0.0212017944752693 \tabularnewline
72 & 16 & 13.2219204118174 & 2.77807958818264 \tabularnewline
73 & 15 & 14.0181212443233 & 0.981878755676731 \tabularnewline
74 & 15 & 15.2131926306201 & -0.213192630620136 \tabularnewline
75 & 13 & 14.8127818017532 & -1.81278180175318 \tabularnewline
76 & 12 & 11.6202770963495 & 0.379722903650464 \tabularnewline
77 & 17 & 14.0119601440193 & 2.98803985598073 \tabularnewline
78 & 13 & 12.4149376537794 & 0.585062346220553 \tabularnewline
79 & 15 & 14.0088795938673 & 0.991120406132731 \tabularnewline
80 & 13 & 15.6028215339551 & -2.60282153395509 \tabularnewline
81 & 15 & 14.8035401512972 & 0.19645984870282 \tabularnewline
82 & 16 & 15.2008704300121 & 0.799129569987865 \tabularnewline
83 & 15 & 15.5982007087271 & -0.598200708727091 \tabularnewline
84 & 16 & 14.4000487722782 & 1.59995122772178 \tabularnewline
85 & 15 & 14.3985084972022 & 0.601491502797776 \tabularnewline
86 & 14 & 13.9980976683353 & 0.00190233166473155 \tabularnewline
87 & 15 & 13.9965573932593 & 1.00344260674073 \tabularnewline
88 & 14 & 14.3938876719742 & -0.393887671974224 \tabularnewline
89 & 13 & 13.1957357355254 & -0.195735735525357 \tabularnewline
90 & 7 & 10.4021015839127 & -3.40210158391267 \tabularnewline
91 & 17 & 13.9903962929553 & 3.00960370704473 \tabularnewline
92 & 13 & 13.1911149102974 & -0.191114910297357 \tabularnewline
93 & 15 & 13.9873157428033 & 1.01268425719673 \tabularnewline
94 & 14 & 13.1880343601454 & 0.811965639854643 \tabularnewline
95 & 13 & 15.1808468540241 & -2.18084685402413 \tabularnewline
96 & 16 & 14.7804360251572 & 1.21956397484282 \tabularnewline
97 & 12 & 12.7845429811264 & -0.784542981126401 \tabularnewline
98 & 14 & 14.7773554750052 & -0.777355475005179 \tabularnewline
99 & 17 & 14.7758151999292 & 2.22418480007082 \tabularnewline
100 & 15 & 15.1731454786441 & -0.173145478644134 \tabularnewline
101 & 17 & 15.1716052035681 & 1.82839479643187 \tabularnewline
102 & 12 & 12.7768416057464 & -0.7768416057464 \tabularnewline
103 & 16 & 14.3707835458342 & 1.62921645416578 \tabularnewline
104 & 11 & 14.7681138245492 & -3.76811382454918 \tabularnewline
105 & 15 & 12.7722207805184 & 2.2277792194816 \tabularnewline
106 & 9 & 11.5740688440695 & -2.57406884406953 \tabularnewline
107 & 16 & 14.7634929993212 & 1.23650700067882 \tabularnewline
108 & 15 & 12.7675999552904 & 2.2324000447096 \tabularnewline
109 & 10 & 13.1649302340054 & -3.16493023400536 \tabularnewline
110 & 10 & 9.57355497481076 & 0.426445025189244 \tabularnewline
111 & 15 & 13.5607202376443 & 1.43927976235569 \tabularnewline
112 & 11 & 13.1603094087774 & -2.16030940877736 \tabularnewline
113 & 13 & 14.7542513488652 & -1.75425134886518 \tabularnewline
114 & 14 & 13.1572288586254 & 0.842771141374644 \tabularnewline
115 & 18 & 14.3523002449222 & 3.64769975507778 \tabularnewline
116 & 16 & 15.1485010774281 & 0.851498922571867 \tabularnewline
117 & 14 & 13.5514785871883 & 0.448521412811689 \tabularnewline
118 & 14 & 13.9488088659033 & 0.0511911340967334 \tabularnewline
119 & 14 & 15.5427508059911 & -1.54275080599109 \tabularnewline
120 & 14 & 13.1479872081694 & 0.852012791830645 \tabularnewline
121 & 12 & 13.1464469330934 & -1.14644693309336 \tabularnewline
122 & 14 & 13.5437772118083 & 0.456222788191689 \tabularnewline
123 & 15 & 14.7388485981052 & 0.261151401894822 \tabularnewline
124 & 15 & 15.933919984402 & -0.933919984402044 \tabularnewline
125 & 15 & 14.7357680479532 & 0.264231952046823 \tabularnewline
126 & 13 & 14.7342277728772 & -1.73422777287718 \tabularnewline
127 & 17 & 15.929299159174 & 1.07070084082596 \tabularnewline
128 & 17 & 15.1300177765161 & 1.86998222348387 \tabularnewline
129 & 19 & 14.7296069476492 & 4.27039305235082 \tabularnewline
130 & 15 & 13.9303255649913 & 1.06967443500873 \tabularnewline
131 & 13 & 14.7265263974972 & -1.72652639749718 \tabularnewline
132 & 9 & 11.1351511383026 & -2.13515113830258 \tabularnewline
133 & 15 & 15.1223164011361 & -0.122316401136132 \tabularnewline
134 & 15 & 13.1264233571054 & 1.87357664289465 \tabularnewline
135 & 15 & 14.3214947434022 & 0.678505256597779 \tabularnewline
136 & 16 & 13.5222133607443 & 2.47778663925569 \tabularnewline
137 & 11 & 9.93083810154971 & 1.06916189845029 \tabularnewline
138 & 14 & 13.5191328105923 & 0.48086718940769 \tabularnewline
139 & 11 & 12.7198514279344 & -1.7198514279344 \tabularnewline
140 & 15 & 14.7126639218132 & 0.287336078186823 \tabularnewline
141 & 13 & 14.3122530929462 & -1.31225309294622 \tabularnewline
142 & 15 & 15.1084539254521 & -0.108453925452132 \tabularnewline
143 & 16 & 13.5114314352123 & 2.48856856478769 \tabularnewline
144 & 14 & 14.3076322677182 & -0.307632267718221 \tabularnewline
145 & 15 & 14.3060919926422 & 0.693908007357779 \tabularnewline
146 & 16 & 14.7034222713572 & 1.29657772864282 \tabularnewline
147 & 16 & 14.3030114424902 & 1.69698855750978 \tabularnewline
148 & 11 & 13.9026006136233 & -2.90260061362326 \tabularnewline
149 & 12 & 14.6988014461292 & -2.69880144612918 \tabularnewline
150 & 9 & 11.5062967407255 & -2.50629674072553 \tabularnewline
151 & 16 & 14.2968503421862 & 1.70314965781378 \tabularnewline
152 & 13 & 12.3009572981554 & 0.699042701844558 \tabularnewline
153 & 16 & 15.4903814534071 & 0.509618546592913 \tabularnewline
154 & 12 & 14.2922295169582 & -2.29222951695822 \tabularnewline
155 & 9 & 11.4985953653455 & -2.49859536534553 \tabularnewline
156 & 13 & 11.8959256440605 & 1.10407435593951 \tabularnewline
157 & 13 & 13.0909970303574 & -0.0909970303573532 \tabularnewline
158 & 14 & 13.4883273090723 & 0.511672690927691 \tabularnewline
159 & 19 & 14.6833986953692 & 4.31660130463082 \tabularnewline
160 & 13 & 15.4795995278751 & -2.47959952787509 \tabularnewline
161 & 12 & 11.8882242686805 & 0.111775731319514 \tabularnewline
162 & 13 & 12.6844251011864 & 0.315574898813603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200574&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]14.5278916035863[/C][C]-0.527891603586283[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]14.9252218823012[/C][C]3.07477811769882[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.7270699458523[/C][C]-2.72706994585232[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.5232707783582[/C][C]-2.52327077835823[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]10.9318955191636[/C][C]5.06810448083637[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.5201902282062[/C][C]3.47980977179377[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.5299444152207[/C][C]3.47005558477933[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.9159802318452[/C][C]-0.915980231845184[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.3133105105601[/C][C]-0.31331051056014[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.1151585741113[/C][C]0.884841425888727[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.3102299604081[/C][C]1.68977003959186[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]16.1064307929141[/C][C]2.89356920708595[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.3127966413014[/C][C]-3.31279664130136[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.7101269200163[/C][C]2.28987307998368[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.3040688601041[/C][C]2.69593113989586[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.7070463698643[/C][C]0.292953630135683[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.7055060947883[/C][C]0.294493905211683[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]14.9005774810852[/C][C]2.09942251891482[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.2979077598001[/C][C]-1.29790775980014[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.0997558233513[/C][C]1.90024417664873[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]16.491438871021[/C][C]1.50856112897899[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.6978047194083[/C][C]-2.69780471940832[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.4940055519142[/C][C]-0.494005551914228[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.6947241692563[/C][C]-1.69472416925632[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.8897955555532[/C][C]2.11020444444682[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.6859963880591[/C][C]-6.68599638805909[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.8867150054012[/C][C]1.11328499459882[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.2896925151614[/C][C]0.710307484838639[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.6870227938763[/C][C]1.31297720612368[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]14.0843530725913[/C][C]-3.08435307259127[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.6782950126791[/C][C]0.321704987320906[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]13.2835314148574[/C][C]-0.28353141485736[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.2763439087361[/C][C]1.72365609126386[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.8759330798692[/C][C]0.124066920130817[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.0766516972113[/C][C]-0.0766516972112713[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]16.06946419109[/C][C]-0.069464191090049[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]11.2814772705226[/C][C]-2.28147727052258[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.4709014257742[/C][C]0.529098574225773[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.2671022582801[/C][C]1.73289774171986[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.2655619832041[/C][C]-2.26556198320414[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.6628922619191[/C][C]-0.662892261919093[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.6669992178883[/C][C]2.33300078211168[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]16.058682265558[/C][C]-0.0586822655580486[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.6639186677363[/C][C]-1.66391866773632[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.8589900540332[/C][C]-2.85899005403318[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.0597086713753[/C][C]-3.05970867137527[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.6536506114631[/C][C]-0.653650611463093[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8543692288052[/C][C]0.145630771194818[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.2573467385654[/C][C]3.74265326143464[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.8512886786532[/C][C]-1.85128867865318[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.2486189573681[/C][C]0.751381042631863[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.6515964671283[/C][C]0.348403532871685[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]12.0545739768885[/C][C]-1.05457397688849[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.6485159169763[/C][C]-1.64851591697631[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.8435873032732[/C][C]-2.84358730327318[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.4431764744062[/C][C]0.556823525593774[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.0427656455393[/C][C]1.95723435446073[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.6367075856271[/C][C]-0.636707585627092[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.2362967567601[/C][C]-3.23629675676014[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.2404037127294[/C][C]-1.24040371272936[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]11.2445106686986[/C][C]-3.24451066869858[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.4339348239502[/C][C]-1.43393482395023[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.4323945488742[/C][C]-3.43239454887423[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.6331131662163[/C][C]0.366886833783686[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.0304434449313[/C][C]0.969556555068731[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.8266442774372[/C][C]-4.82664427743718[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.4318806796154[/C][C]-1.43188067961545[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.4246931734942[/C][C]-2.42469317349422[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.0242823446273[/C][C]0.975717655372731[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.6238715157603[/C][C]1.37612848423969[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]14.0212017944753[/C][C]-0.0212017944752693[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.2219204118174[/C][C]2.77807958818264[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.0181212443233[/C][C]0.981878755676731[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.2131926306201[/C][C]-0.213192630620136[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.8127818017532[/C][C]-1.81278180175318[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.6202770963495[/C][C]0.379722903650464[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.0119601440193[/C][C]2.98803985598073[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.4149376537794[/C][C]0.585062346220553[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.0088795938673[/C][C]0.991120406132731[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]15.6028215339551[/C][C]-2.60282153395509[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.8035401512972[/C][C]0.19645984870282[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.2008704300121[/C][C]0.799129569987865[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.5982007087271[/C][C]-0.598200708727091[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.4000487722782[/C][C]1.59995122772178[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.3985084972022[/C][C]0.601491502797776[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.9980976683353[/C][C]0.00190233166473155[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]13.9965573932593[/C][C]1.00344260674073[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.3938876719742[/C][C]-0.393887671974224[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.1957357355254[/C][C]-0.195735735525357[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.4021015839127[/C][C]-3.40210158391267[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]13.9903962929553[/C][C]3.00960370704473[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.1911149102974[/C][C]-0.191114910297357[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]13.9873157428033[/C][C]1.01268425719673[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.1880343601454[/C][C]0.811965639854643[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1808468540241[/C][C]-2.18084685402413[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.7804360251572[/C][C]1.21956397484282[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.7845429811264[/C][C]-0.784542981126401[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.7773554750052[/C][C]-0.777355475005179[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.7758151999292[/C][C]2.22418480007082[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]15.1731454786441[/C][C]-0.173145478644134[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.1716052035681[/C][C]1.82839479643187[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.7768416057464[/C][C]-0.7768416057464[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.3707835458342[/C][C]1.62921645416578[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.7681138245492[/C][C]-3.76811382454918[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]12.7722207805184[/C][C]2.2277792194816[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.5740688440695[/C][C]-2.57406884406953[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.7634929993212[/C][C]1.23650700067882[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.7675999552904[/C][C]2.2324000447096[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]13.1649302340054[/C][C]-3.16493023400536[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.57355497481076[/C][C]0.426445025189244[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]13.5607202376443[/C][C]1.43927976235569[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.1603094087774[/C][C]-2.16030940877736[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.7542513488652[/C][C]-1.75425134886518[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.1572288586254[/C][C]0.842771141374644[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.3523002449222[/C][C]3.64769975507778[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.1485010774281[/C][C]0.851498922571867[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.5514785871883[/C][C]0.448521412811689[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.9488088659033[/C][C]0.0511911340967334[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.5427508059911[/C][C]-1.54275080599109[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]13.1479872081694[/C][C]0.852012791830645[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.1464469330934[/C][C]-1.14644693309336[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.5437772118083[/C][C]0.456222788191689[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]14.7388485981052[/C][C]0.261151401894822[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.933919984402[/C][C]-0.933919984402044[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.7357680479532[/C][C]0.264231952046823[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.7342277728772[/C][C]-1.73422777287718[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.929299159174[/C][C]1.07070084082596[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.1300177765161[/C][C]1.86998222348387[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]14.7296069476492[/C][C]4.27039305235082[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.9303255649913[/C][C]1.06967443500873[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.7265263974972[/C][C]-1.72652639749718[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]11.1351511383026[/C][C]-2.13515113830258[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.1223164011361[/C][C]-0.122316401136132[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.1264233571054[/C][C]1.87357664289465[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.3214947434022[/C][C]0.678505256597779[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.5222133607443[/C][C]2.47778663925569[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]9.93083810154971[/C][C]1.06916189845029[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.5191328105923[/C][C]0.48086718940769[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.7198514279344[/C][C]-1.7198514279344[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.7126639218132[/C][C]0.287336078186823[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]14.3122530929462[/C][C]-1.31225309294622[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.1084539254521[/C][C]-0.108453925452132[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.5114314352123[/C][C]2.48856856478769[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.3076322677182[/C][C]-0.307632267718221[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.3060919926422[/C][C]0.693908007357779[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.7034222713572[/C][C]1.29657772864282[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.3030114424902[/C][C]1.69698855750978[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.9026006136233[/C][C]-2.90260061362326[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.6988014461292[/C][C]-2.69880144612918[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.5062967407255[/C][C]-2.50629674072553[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]14.2968503421862[/C][C]1.70314965781378[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.3009572981554[/C][C]0.699042701844558[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.4903814534071[/C][C]0.509618546592913[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.2922295169582[/C][C]-2.29222951695822[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.4985953653455[/C][C]-2.49859536534553[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]11.8959256440605[/C][C]1.10407435593951[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]13.0909970303574[/C][C]-0.0909970303573532[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.4883273090723[/C][C]0.511672690927691[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]14.6833986953692[/C][C]4.31660130463082[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.4795995278751[/C][C]-2.47959952787509[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]11.8882242686805[/C][C]0.111775731319514[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6844251011864[/C][C]0.315574898813603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200574&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200574&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.5278916035863-0.527891603586283
21814.92522188230123.07477811769882
31113.7270699458523-2.72706994585232
41214.5232707783582-2.52327077835823
51610.93189551916365.06810448083637
61814.52019022820623.47980977179377
71410.52994441522073.47005558477933
81414.9159802318452-0.915980231845184
91515.3133105105601-0.31331051056014
101514.11515857411130.884841425888727
111715.31022996040811.68977003959186
121916.10643079291412.89356920708595
131013.3127966413014-3.31279664130136
141613.71012692001632.28987307998368
151815.30406886010412.69593113989586
161413.70704636986430.292953630135683
171413.70550609478830.294493905211683
181714.90057748108522.09942251891482
191415.2979077598001-1.29790775980014
201614.09975582335131.90024417664873
211816.4914388710211.50856112897899
221113.6978047194083-2.69780471940832
231414.4940055519142-0.494005551914228
241213.6947241692563-1.69472416925632
251714.88979555555322.11020444444682
26915.6859963880591-6.68599638805909
271614.88671500540121.11328499459882
281413.28969251516140.710307484838639
291513.68702279387631.31297720612368
301114.0843530725913-3.08435307259127
311615.67829501267910.321704987320906
321313.2835314148574-0.28353141485736
331715.27634390873611.72365609126386
341514.87593307986920.124066920130817
351414.0766516972113-0.0766516972112713
361616.06946419109-0.069464191090049
37911.2814772705226-2.28147727052258
381514.47090142577420.529098574225773
391715.26710225828011.73289774171986
401315.2655619832041-2.26556198320414
411515.6628922619191-0.662892261919093
421613.66699921788832.33300078211168
431616.058682265558-0.0586822655580486
441213.6639186677363-1.66391866773632
451214.8589900540332-2.85899005403318
461114.0597086713753-3.05970867137527
471515.6536506114631-0.653650611463093
481514.85436922880520.145630771194818
491713.25734673856543.74265326143464
501314.8512886786532-1.85128867865318
511615.24861895736810.751381042631863
521413.65159646712830.348403532871685
531112.0545739768885-1.05457397688849
541213.6485159169763-1.64851591697631
551214.8435873032732-2.84358730327318
561514.44317647440620.556823525593774
571614.04276564553931.95723435446073
581515.6367075856271-0.636707585627092
591215.2362967567601-3.23629675676014
601213.2404037127294-1.24040371272936
61811.2445106686986-3.24451066869858
621314.4339348239502-1.43393482395023
631114.4323945488742-3.43239454887423
641413.63311316621630.366886833783686
651514.03044344493130.969556555068731
661014.8266442774372-4.82664427743718
671112.4318806796154-1.43188067961545
681214.4246931734942-2.42469317349422
691514.02428234462730.975717655372731
701513.62387151576031.37612848423969
711414.0212017944753-0.0212017944752693
721613.22192041181742.77807958818264
731514.01812124432330.981878755676731
741515.2131926306201-0.213192630620136
751314.8127818017532-1.81278180175318
761211.62027709634950.379722903650464
771714.01196014401932.98803985598073
781312.41493765377940.585062346220553
791514.00887959386730.991120406132731
801315.6028215339551-2.60282153395509
811514.80354015129720.19645984870282
821615.20087043001210.799129569987865
831515.5982007087271-0.598200708727091
841614.40004877227821.59995122772178
851514.39850849720220.601491502797776
861413.99809766833530.00190233166473155
871513.99655739325931.00344260674073
881414.3938876719742-0.393887671974224
891313.1957357355254-0.195735735525357
90710.4021015839127-3.40210158391267
911713.99039629295533.00960370704473
921313.1911149102974-0.191114910297357
931513.98731574280331.01268425719673
941413.18803436014540.811965639854643
951315.1808468540241-2.18084685402413
961614.78043602515721.21956397484282
971212.7845429811264-0.784542981126401
981414.7773554750052-0.777355475005179
991714.77581519992922.22418480007082
1001515.1731454786441-0.173145478644134
1011715.17160520356811.82839479643187
1021212.7768416057464-0.7768416057464
1031614.37078354583421.62921645416578
1041114.7681138245492-3.76811382454918
1051512.77222078051842.2277792194816
106911.5740688440695-2.57406884406953
1071614.76349299932121.23650700067882
1081512.76759995529042.2324000447096
1091013.1649302340054-3.16493023400536
110109.573554974810760.426445025189244
1111513.56072023764431.43927976235569
1121113.1603094087774-2.16030940877736
1131314.7542513488652-1.75425134886518
1141413.15722885862540.842771141374644
1151814.35230024492223.64769975507778
1161615.14850107742810.851498922571867
1171413.55147858718830.448521412811689
1181413.94880886590330.0511911340967334
1191415.5427508059911-1.54275080599109
1201413.14798720816940.852012791830645
1211213.1464469330934-1.14644693309336
1221413.54377721180830.456222788191689
1231514.73884859810520.261151401894822
1241515.933919984402-0.933919984402044
1251514.73576804795320.264231952046823
1261314.7342277728772-1.73422777287718
1271715.9292991591741.07070084082596
1281715.13001777651611.86998222348387
1291914.72960694764924.27039305235082
1301513.93032556499131.06967443500873
1311314.7265263974972-1.72652639749718
132911.1351511383026-2.13515113830258
1331515.1223164011361-0.122316401136132
1341513.12642335710541.87357664289465
1351514.32149474340220.678505256597779
1361613.52221336074432.47778663925569
137119.930838101549711.06916189845029
1381413.51913281059230.48086718940769
1391112.7198514279344-1.7198514279344
1401514.71266392181320.287336078186823
1411314.3122530929462-1.31225309294622
1421515.1084539254521-0.108453925452132
1431613.51143143521232.48856856478769
1441414.3076322677182-0.307632267718221
1451514.30609199264220.693908007357779
1461614.70342227135721.29657772864282
1471614.30301144249021.69698855750978
1481113.9026006136233-2.90260061362326
1491214.6988014461292-2.69880144612918
150911.5062967407255-2.50629674072553
1511614.29685034218621.70314965781378
1521312.30095729815540.699042701844558
1531615.49038145340710.509618546592913
1541214.2922295169582-2.29222951695822
155911.4985953653455-2.49859536534553
1561311.89592564406051.10407435593951
1571313.0909970303574-0.0909970303573532
1581413.48832730907230.511672690927691
1591914.68339869536924.31660130463082
1601315.4795995278751-2.47959952787509
1611211.88822426868050.111775731319514
1621312.68442510118640.315574898813603







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9878179144294780.02436417114104330.0121820855705216
70.9787991010658260.04240179786834790.021200898934174
80.9699728673909680.06005426521806370.0300271326090319
90.9451414128013510.1097171743972980.0548585871986492
100.9096926908835090.1806146182329830.0903073091164913
110.8842852135866550.2314295728266890.115714786413345
120.8927724756811590.2144550486376820.107227524318841
130.9858387656509880.02832246869802360.0141612343490118
140.9806427775585070.03871444488298670.0193572224414933
150.9786910045923180.04261799081536430.0213089954076821
160.9716880460963550.05662390780729030.0283119539036451
170.961236458462650.07752708307470030.0387635415373502
180.9515805780155940.09683884396881160.0484194219844058
190.9479521105473080.1040957789053840.0520478894526919
200.934134687445840.131730625108320.0658653125541601
210.9190937815938380.1618124368123250.0809062184061624
220.956794040738240.08641191852352070.0432059592617603
230.9426164888757550.114767022248490.0573835111242452
240.9384109537129280.1231780925741450.0615890462870724
250.9401178777215990.1197642445568030.0598821222784013
260.9962388910844360.007522217831128590.0037611089155643
270.9956521719813480.008695656037303520.00434782801865176
280.9939228010636380.01215439787272440.00607719893636221
290.9926639838829020.01467203223419690.00733601611709846
300.9941598369885090.0116803260229820.00584016301149102
310.992390709269030.01521858146194040.00760929073097018
320.9889603662778550.02207926744428920.0110396337221446
330.9901003565027740.01979928699445180.00989964349722591
340.986320798448060.02735840310387910.0136792015519395
350.9809849044794540.03803019104109230.0190150955205462
360.974613267971680.05077346405663950.0253867320283198
370.9747976365294530.05040472694109470.0252023634705473
380.9688930792045180.06221384159096390.0311069207954819
390.9710496715769970.05790065684600690.0289503284230035
400.9682008435846670.06359831283066640.0317991564153332
410.9579271083836380.0841457832327240.042072891616362
420.9672561796300560.06548764073988760.0327438203699438
430.9576000818293730.08479983634125390.0423999181706269
440.949683813370160.1006323732596790.0503161866298397
450.9523157218282150.09536855634357060.0476842781717853
460.9565550709522710.08688985809545870.0434449290477294
470.9446478921999610.1107042156000780.055352107800039
480.9333783236599240.1332433526801520.0666216763400759
490.9738501094846120.05229978103077550.0261498905153878
500.9689570628884850.06208587422303020.0310429371115151
510.9644352659701010.07112946805979760.0355647340298988
520.955999473470640.08800105305871950.0440005265293598
530.945137161949230.109725676101540.0548628380507702
540.9349835188657460.1300329622685080.0650164811342539
550.9377065710900410.1245868578199180.0622934289099589
560.9286816811698740.1426366376602530.0713183188301263
570.9387437309792670.1225125380414660.061256269020733
580.9236908309128150.152618338174370.0763091690871852
590.9351820503260020.1296358993479950.0648179496739976
600.9211099485572090.1577801028855820.0788900514427912
610.9339720778409530.1320558443180940.066027922159047
620.9215461307890950.156907738421810.0784538692109048
630.937776197587960.124447604824080.0622238024120401
640.9289204543573110.1421590912853780.0710795456426888
650.9256318940170570.1487362119658850.0743681059829425
660.9701151355808620.05976972883827660.0298848644191383
670.9643641805674710.07127163886505780.0356358194325289
680.9660122471502120.06797550569957540.0339877528497877
690.9648910180571950.07021796388561010.0351089819428051
700.9659481570175770.0681036859648460.034051842982423
710.9587982585079510.08240348298409750.0412017414920487
720.973986712068070.05202657586385990.0260132879319299
730.9710851832336070.05782963353278620.0289148167663931
740.9642571849543120.07148563009137620.0357428150456881
750.9619101791346020.07617964173079590.038089820865398
760.9530573927079050.09388521458418940.0469426072920947
770.9710688037274790.05786239254504150.0289311962725207
780.9645723665554520.07085526688909680.0354276334445484
790.9596362214190540.08072755716189250.0403637785809463
800.9657552955070230.0684894089859540.034244704492977
810.9578269573730460.08434608525390840.0421730426269542
820.9510706440536660.09785871189266760.0489293559463338
830.9414468041571510.1171063916856990.0585531958428495
840.9395572063548220.1208855872903550.0604427936451777
850.9276448252021280.1447103495957430.0723551747978716
860.9111984365054080.1776031269891840.088801563494592
870.8988386492600320.2023227014799370.101161350739968
880.8784705588798030.2430588822403940.121529441120197
890.8539031468254960.2921937063490080.146096853174504
900.8940548962763260.2118902074473480.105945103723674
910.9231187105934950.153762578813010.0768812894065049
920.9053375259030530.1893249481938930.0946624740969465
930.8916979436656710.2166041126686590.108302056334329
940.8742071337216730.2515857325566540.125792866278327
950.8811225193036790.2377549613926430.118877480696321
960.8674088732062390.2651822535875220.132591126793761
970.8445025958382310.3109948083235380.155497404161769
980.8218327604577770.3563344790844450.178167239542223
990.8304991827540010.3390016344919980.169500817245999
1000.8006831339090450.3986337321819110.199316866090955
1010.7958138716008610.4083722567982770.204186128399139
1020.7652394138783990.4695211722432030.234760586121601
1030.7543691313665160.4912617372669690.245630868633484
1040.8497727365608820.3004545268782360.150227263439118
1050.858964674227870.2820706515442590.14103532577213
1060.8768217365061310.2463565269877380.123178263493869
1070.8595936309314770.2808127381370460.140406369068523
1080.8685536188314690.2628927623370610.131446381168531
1090.9105895820265350.1788208359469310.0894104179734655
1100.8902699233890840.2194601532218320.109730076610916
1110.8782224945203620.2435550109592760.121777505479638
1120.8882139557558040.2235720884883910.111786044244196
1130.893839427237850.2123211455242990.10616057276215
1140.8715610046596230.2568779906807540.128438995340377
1150.9220003922846130.1559992154307740.0779996077153869
1160.9040850215613460.1918299568773070.0959149784386536
1170.8808453304770980.2383093390458050.119154669522902
1180.8526127985322080.2947744029355840.147387201467792
1190.8507385617581830.2985228764836350.149261438241817
1200.8227054553813510.3545890892372990.177294544618649
1210.8029437926546450.394112414690710.197056207345355
1220.7640634624744340.4718730750511310.235936537525566
1230.7209201766953850.5581596466092290.279079823304615
1240.7023395068010580.5953209863978850.297660493198942
1250.6548377898593880.6903244202812230.345162210140611
1260.6793121053771920.6413757892456160.320687894622808
1270.6323915098984610.7352169802030780.367608490101539
1280.6000945378477730.7998109243044540.399905462152227
1290.7540593059594850.4918813880810290.245940694040515
1300.7181608635570280.5636782728859440.281839136442972
1310.7179029648274870.5641940703450260.282097035172513
1320.7375388761561590.5249222476876830.262461123843841
1330.691446991548710.6171060169025810.30855300845129
1340.6686250619402440.6627498761195120.331374938059756
1350.6111181954822570.7777636090354860.388881804517743
1360.6396287505872740.7207424988254510.360371249412726
1370.6259533143563070.7480933712873870.374046685643693
1380.5752789782906110.8494420434187770.424721021709389
1390.5323489512777720.9353020974444550.467651048722228
1400.4650833831194680.9301667662389370.534916616880531
1410.4223133713415970.8446267426831940.577686628658403
1420.3551893556223780.7103787112447550.644810644377622
1430.4165179311437020.8330358622874030.583482068856298
1440.3443697000656980.6887394001313950.655630299934302
1450.2958642309488080.5917284618976160.704135769051192
1460.2864952753649050.5729905507298110.713504724635095
1470.3638150446025440.7276300892050880.636184955397456
1480.3335145897585460.6670291795170910.666485410241454
1490.3389388660073990.6778777320147980.661061133992601
1500.3271067484421290.6542134968842580.672893251557871
1510.2993500962838650.598700192567730.700649903716135
1520.2508746420551530.5017492841103060.749125357944847
1530.1978140509528820.3956281019057640.802185949047118
1540.161444401714770.3228888034295390.83855559828523
1550.204490082877770.408980165755540.79550991712223
1560.1149921316460460.2299842632920910.885007868353954

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.987817914429478 & 0.0243641711410433 & 0.0121820855705216 \tabularnewline
7 & 0.978799101065826 & 0.0424017978683479 & 0.021200898934174 \tabularnewline
8 & 0.969972867390968 & 0.0600542652180637 & 0.0300271326090319 \tabularnewline
9 & 0.945141412801351 & 0.109717174397298 & 0.0548585871986492 \tabularnewline
10 & 0.909692690883509 & 0.180614618232983 & 0.0903073091164913 \tabularnewline
11 & 0.884285213586655 & 0.231429572826689 & 0.115714786413345 \tabularnewline
12 & 0.892772475681159 & 0.214455048637682 & 0.107227524318841 \tabularnewline
13 & 0.985838765650988 & 0.0283224686980236 & 0.0141612343490118 \tabularnewline
14 & 0.980642777558507 & 0.0387144448829867 & 0.0193572224414933 \tabularnewline
15 & 0.978691004592318 & 0.0426179908153643 & 0.0213089954076821 \tabularnewline
16 & 0.971688046096355 & 0.0566239078072903 & 0.0283119539036451 \tabularnewline
17 & 0.96123645846265 & 0.0775270830747003 & 0.0387635415373502 \tabularnewline
18 & 0.951580578015594 & 0.0968388439688116 & 0.0484194219844058 \tabularnewline
19 & 0.947952110547308 & 0.104095778905384 & 0.0520478894526919 \tabularnewline
20 & 0.93413468744584 & 0.13173062510832 & 0.0658653125541601 \tabularnewline
21 & 0.919093781593838 & 0.161812436812325 & 0.0809062184061624 \tabularnewline
22 & 0.95679404073824 & 0.0864119185235207 & 0.0432059592617603 \tabularnewline
23 & 0.942616488875755 & 0.11476702224849 & 0.0573835111242452 \tabularnewline
24 & 0.938410953712928 & 0.123178092574145 & 0.0615890462870724 \tabularnewline
25 & 0.940117877721599 & 0.119764244556803 & 0.0598821222784013 \tabularnewline
26 & 0.996238891084436 & 0.00752221783112859 & 0.0037611089155643 \tabularnewline
27 & 0.995652171981348 & 0.00869565603730352 & 0.00434782801865176 \tabularnewline
28 & 0.993922801063638 & 0.0121543978727244 & 0.00607719893636221 \tabularnewline
29 & 0.992663983882902 & 0.0146720322341969 & 0.00733601611709846 \tabularnewline
30 & 0.994159836988509 & 0.011680326022982 & 0.00584016301149102 \tabularnewline
31 & 0.99239070926903 & 0.0152185814619404 & 0.00760929073097018 \tabularnewline
32 & 0.988960366277855 & 0.0220792674442892 & 0.0110396337221446 \tabularnewline
33 & 0.990100356502774 & 0.0197992869944518 & 0.00989964349722591 \tabularnewline
34 & 0.98632079844806 & 0.0273584031038791 & 0.0136792015519395 \tabularnewline
35 & 0.980984904479454 & 0.0380301910410923 & 0.0190150955205462 \tabularnewline
36 & 0.97461326797168 & 0.0507734640566395 & 0.0253867320283198 \tabularnewline
37 & 0.974797636529453 & 0.0504047269410947 & 0.0252023634705473 \tabularnewline
38 & 0.968893079204518 & 0.0622138415909639 & 0.0311069207954819 \tabularnewline
39 & 0.971049671576997 & 0.0579006568460069 & 0.0289503284230035 \tabularnewline
40 & 0.968200843584667 & 0.0635983128306664 & 0.0317991564153332 \tabularnewline
41 & 0.957927108383638 & 0.084145783232724 & 0.042072891616362 \tabularnewline
42 & 0.967256179630056 & 0.0654876407398876 & 0.0327438203699438 \tabularnewline
43 & 0.957600081829373 & 0.0847998363412539 & 0.0423999181706269 \tabularnewline
44 & 0.94968381337016 & 0.100632373259679 & 0.0503161866298397 \tabularnewline
45 & 0.952315721828215 & 0.0953685563435706 & 0.0476842781717853 \tabularnewline
46 & 0.956555070952271 & 0.0868898580954587 & 0.0434449290477294 \tabularnewline
47 & 0.944647892199961 & 0.110704215600078 & 0.055352107800039 \tabularnewline
48 & 0.933378323659924 & 0.133243352680152 & 0.0666216763400759 \tabularnewline
49 & 0.973850109484612 & 0.0522997810307755 & 0.0261498905153878 \tabularnewline
50 & 0.968957062888485 & 0.0620858742230302 & 0.0310429371115151 \tabularnewline
51 & 0.964435265970101 & 0.0711294680597976 & 0.0355647340298988 \tabularnewline
52 & 0.95599947347064 & 0.0880010530587195 & 0.0440005265293598 \tabularnewline
53 & 0.94513716194923 & 0.10972567610154 & 0.0548628380507702 \tabularnewline
54 & 0.934983518865746 & 0.130032962268508 & 0.0650164811342539 \tabularnewline
55 & 0.937706571090041 & 0.124586857819918 & 0.0622934289099589 \tabularnewline
56 & 0.928681681169874 & 0.142636637660253 & 0.0713183188301263 \tabularnewline
57 & 0.938743730979267 & 0.122512538041466 & 0.061256269020733 \tabularnewline
58 & 0.923690830912815 & 0.15261833817437 & 0.0763091690871852 \tabularnewline
59 & 0.935182050326002 & 0.129635899347995 & 0.0648179496739976 \tabularnewline
60 & 0.921109948557209 & 0.157780102885582 & 0.0788900514427912 \tabularnewline
61 & 0.933972077840953 & 0.132055844318094 & 0.066027922159047 \tabularnewline
62 & 0.921546130789095 & 0.15690773842181 & 0.0784538692109048 \tabularnewline
63 & 0.93777619758796 & 0.12444760482408 & 0.0622238024120401 \tabularnewline
64 & 0.928920454357311 & 0.142159091285378 & 0.0710795456426888 \tabularnewline
65 & 0.925631894017057 & 0.148736211965885 & 0.0743681059829425 \tabularnewline
66 & 0.970115135580862 & 0.0597697288382766 & 0.0298848644191383 \tabularnewline
67 & 0.964364180567471 & 0.0712716388650578 & 0.0356358194325289 \tabularnewline
68 & 0.966012247150212 & 0.0679755056995754 & 0.0339877528497877 \tabularnewline
69 & 0.964891018057195 & 0.0702179638856101 & 0.0351089819428051 \tabularnewline
70 & 0.965948157017577 & 0.068103685964846 & 0.034051842982423 \tabularnewline
71 & 0.958798258507951 & 0.0824034829840975 & 0.0412017414920487 \tabularnewline
72 & 0.97398671206807 & 0.0520265758638599 & 0.0260132879319299 \tabularnewline
73 & 0.971085183233607 & 0.0578296335327862 & 0.0289148167663931 \tabularnewline
74 & 0.964257184954312 & 0.0714856300913762 & 0.0357428150456881 \tabularnewline
75 & 0.961910179134602 & 0.0761796417307959 & 0.038089820865398 \tabularnewline
76 & 0.953057392707905 & 0.0938852145841894 & 0.0469426072920947 \tabularnewline
77 & 0.971068803727479 & 0.0578623925450415 & 0.0289311962725207 \tabularnewline
78 & 0.964572366555452 & 0.0708552668890968 & 0.0354276334445484 \tabularnewline
79 & 0.959636221419054 & 0.0807275571618925 & 0.0403637785809463 \tabularnewline
80 & 0.965755295507023 & 0.068489408985954 & 0.034244704492977 \tabularnewline
81 & 0.957826957373046 & 0.0843460852539084 & 0.0421730426269542 \tabularnewline
82 & 0.951070644053666 & 0.0978587118926676 & 0.0489293559463338 \tabularnewline
83 & 0.941446804157151 & 0.117106391685699 & 0.0585531958428495 \tabularnewline
84 & 0.939557206354822 & 0.120885587290355 & 0.0604427936451777 \tabularnewline
85 & 0.927644825202128 & 0.144710349595743 & 0.0723551747978716 \tabularnewline
86 & 0.911198436505408 & 0.177603126989184 & 0.088801563494592 \tabularnewline
87 & 0.898838649260032 & 0.202322701479937 & 0.101161350739968 \tabularnewline
88 & 0.878470558879803 & 0.243058882240394 & 0.121529441120197 \tabularnewline
89 & 0.853903146825496 & 0.292193706349008 & 0.146096853174504 \tabularnewline
90 & 0.894054896276326 & 0.211890207447348 & 0.105945103723674 \tabularnewline
91 & 0.923118710593495 & 0.15376257881301 & 0.0768812894065049 \tabularnewline
92 & 0.905337525903053 & 0.189324948193893 & 0.0946624740969465 \tabularnewline
93 & 0.891697943665671 & 0.216604112668659 & 0.108302056334329 \tabularnewline
94 & 0.874207133721673 & 0.251585732556654 & 0.125792866278327 \tabularnewline
95 & 0.881122519303679 & 0.237754961392643 & 0.118877480696321 \tabularnewline
96 & 0.867408873206239 & 0.265182253587522 & 0.132591126793761 \tabularnewline
97 & 0.844502595838231 & 0.310994808323538 & 0.155497404161769 \tabularnewline
98 & 0.821832760457777 & 0.356334479084445 & 0.178167239542223 \tabularnewline
99 & 0.830499182754001 & 0.339001634491998 & 0.169500817245999 \tabularnewline
100 & 0.800683133909045 & 0.398633732181911 & 0.199316866090955 \tabularnewline
101 & 0.795813871600861 & 0.408372256798277 & 0.204186128399139 \tabularnewline
102 & 0.765239413878399 & 0.469521172243203 & 0.234760586121601 \tabularnewline
103 & 0.754369131366516 & 0.491261737266969 & 0.245630868633484 \tabularnewline
104 & 0.849772736560882 & 0.300454526878236 & 0.150227263439118 \tabularnewline
105 & 0.85896467422787 & 0.282070651544259 & 0.14103532577213 \tabularnewline
106 & 0.876821736506131 & 0.246356526987738 & 0.123178263493869 \tabularnewline
107 & 0.859593630931477 & 0.280812738137046 & 0.140406369068523 \tabularnewline
108 & 0.868553618831469 & 0.262892762337061 & 0.131446381168531 \tabularnewline
109 & 0.910589582026535 & 0.178820835946931 & 0.0894104179734655 \tabularnewline
110 & 0.890269923389084 & 0.219460153221832 & 0.109730076610916 \tabularnewline
111 & 0.878222494520362 & 0.243555010959276 & 0.121777505479638 \tabularnewline
112 & 0.888213955755804 & 0.223572088488391 & 0.111786044244196 \tabularnewline
113 & 0.89383942723785 & 0.212321145524299 & 0.10616057276215 \tabularnewline
114 & 0.871561004659623 & 0.256877990680754 & 0.128438995340377 \tabularnewline
115 & 0.922000392284613 & 0.155999215430774 & 0.0779996077153869 \tabularnewline
116 & 0.904085021561346 & 0.191829956877307 & 0.0959149784386536 \tabularnewline
117 & 0.880845330477098 & 0.238309339045805 & 0.119154669522902 \tabularnewline
118 & 0.852612798532208 & 0.294774402935584 & 0.147387201467792 \tabularnewline
119 & 0.850738561758183 & 0.298522876483635 & 0.149261438241817 \tabularnewline
120 & 0.822705455381351 & 0.354589089237299 & 0.177294544618649 \tabularnewline
121 & 0.802943792654645 & 0.39411241469071 & 0.197056207345355 \tabularnewline
122 & 0.764063462474434 & 0.471873075051131 & 0.235936537525566 \tabularnewline
123 & 0.720920176695385 & 0.558159646609229 & 0.279079823304615 \tabularnewline
124 & 0.702339506801058 & 0.595320986397885 & 0.297660493198942 \tabularnewline
125 & 0.654837789859388 & 0.690324420281223 & 0.345162210140611 \tabularnewline
126 & 0.679312105377192 & 0.641375789245616 & 0.320687894622808 \tabularnewline
127 & 0.632391509898461 & 0.735216980203078 & 0.367608490101539 \tabularnewline
128 & 0.600094537847773 & 0.799810924304454 & 0.399905462152227 \tabularnewline
129 & 0.754059305959485 & 0.491881388081029 & 0.245940694040515 \tabularnewline
130 & 0.718160863557028 & 0.563678272885944 & 0.281839136442972 \tabularnewline
131 & 0.717902964827487 & 0.564194070345026 & 0.282097035172513 \tabularnewline
132 & 0.737538876156159 & 0.524922247687683 & 0.262461123843841 \tabularnewline
133 & 0.69144699154871 & 0.617106016902581 & 0.30855300845129 \tabularnewline
134 & 0.668625061940244 & 0.662749876119512 & 0.331374938059756 \tabularnewline
135 & 0.611118195482257 & 0.777763609035486 & 0.388881804517743 \tabularnewline
136 & 0.639628750587274 & 0.720742498825451 & 0.360371249412726 \tabularnewline
137 & 0.625953314356307 & 0.748093371287387 & 0.374046685643693 \tabularnewline
138 & 0.575278978290611 & 0.849442043418777 & 0.424721021709389 \tabularnewline
139 & 0.532348951277772 & 0.935302097444455 & 0.467651048722228 \tabularnewline
140 & 0.465083383119468 & 0.930166766238937 & 0.534916616880531 \tabularnewline
141 & 0.422313371341597 & 0.844626742683194 & 0.577686628658403 \tabularnewline
142 & 0.355189355622378 & 0.710378711244755 & 0.644810644377622 \tabularnewline
143 & 0.416517931143702 & 0.833035862287403 & 0.583482068856298 \tabularnewline
144 & 0.344369700065698 & 0.688739400131395 & 0.655630299934302 \tabularnewline
145 & 0.295864230948808 & 0.591728461897616 & 0.704135769051192 \tabularnewline
146 & 0.286495275364905 & 0.572990550729811 & 0.713504724635095 \tabularnewline
147 & 0.363815044602544 & 0.727630089205088 & 0.636184955397456 \tabularnewline
148 & 0.333514589758546 & 0.667029179517091 & 0.666485410241454 \tabularnewline
149 & 0.338938866007399 & 0.677877732014798 & 0.661061133992601 \tabularnewline
150 & 0.327106748442129 & 0.654213496884258 & 0.672893251557871 \tabularnewline
151 & 0.299350096283865 & 0.59870019256773 & 0.700649903716135 \tabularnewline
152 & 0.250874642055153 & 0.501749284110306 & 0.749125357944847 \tabularnewline
153 & 0.197814050952882 & 0.395628101905764 & 0.802185949047118 \tabularnewline
154 & 0.16144440171477 & 0.322888803429539 & 0.83855559828523 \tabularnewline
155 & 0.20449008287777 & 0.40898016575554 & 0.79550991712223 \tabularnewline
156 & 0.114992131646046 & 0.229984263292091 & 0.885007868353954 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200574&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]6[/C][C]0.987817914429478[/C][C]0.0243641711410433[/C][C]0.0121820855705216[/C][/ROW]
[ROW][C]7[/C][C]0.978799101065826[/C][C]0.0424017978683479[/C][C]0.021200898934174[/C][/ROW]
[ROW][C]8[/C][C]0.969972867390968[/C][C]0.0600542652180637[/C][C]0.0300271326090319[/C][/ROW]
[ROW][C]9[/C][C]0.945141412801351[/C][C]0.109717174397298[/C][C]0.0548585871986492[/C][/ROW]
[ROW][C]10[/C][C]0.909692690883509[/C][C]0.180614618232983[/C][C]0.0903073091164913[/C][/ROW]
[ROW][C]11[/C][C]0.884285213586655[/C][C]0.231429572826689[/C][C]0.115714786413345[/C][/ROW]
[ROW][C]12[/C][C]0.892772475681159[/C][C]0.214455048637682[/C][C]0.107227524318841[/C][/ROW]
[ROW][C]13[/C][C]0.985838765650988[/C][C]0.0283224686980236[/C][C]0.0141612343490118[/C][/ROW]
[ROW][C]14[/C][C]0.980642777558507[/C][C]0.0387144448829867[/C][C]0.0193572224414933[/C][/ROW]
[ROW][C]15[/C][C]0.978691004592318[/C][C]0.0426179908153643[/C][C]0.0213089954076821[/C][/ROW]
[ROW][C]16[/C][C]0.971688046096355[/C][C]0.0566239078072903[/C][C]0.0283119539036451[/C][/ROW]
[ROW][C]17[/C][C]0.96123645846265[/C][C]0.0775270830747003[/C][C]0.0387635415373502[/C][/ROW]
[ROW][C]18[/C][C]0.951580578015594[/C][C]0.0968388439688116[/C][C]0.0484194219844058[/C][/ROW]
[ROW][C]19[/C][C]0.947952110547308[/C][C]0.104095778905384[/C][C]0.0520478894526919[/C][/ROW]
[ROW][C]20[/C][C]0.93413468744584[/C][C]0.13173062510832[/C][C]0.0658653125541601[/C][/ROW]
[ROW][C]21[/C][C]0.919093781593838[/C][C]0.161812436812325[/C][C]0.0809062184061624[/C][/ROW]
[ROW][C]22[/C][C]0.95679404073824[/C][C]0.0864119185235207[/C][C]0.0432059592617603[/C][/ROW]
[ROW][C]23[/C][C]0.942616488875755[/C][C]0.11476702224849[/C][C]0.0573835111242452[/C][/ROW]
[ROW][C]24[/C][C]0.938410953712928[/C][C]0.123178092574145[/C][C]0.0615890462870724[/C][/ROW]
[ROW][C]25[/C][C]0.940117877721599[/C][C]0.119764244556803[/C][C]0.0598821222784013[/C][/ROW]
[ROW][C]26[/C][C]0.996238891084436[/C][C]0.00752221783112859[/C][C]0.0037611089155643[/C][/ROW]
[ROW][C]27[/C][C]0.995652171981348[/C][C]0.00869565603730352[/C][C]0.00434782801865176[/C][/ROW]
[ROW][C]28[/C][C]0.993922801063638[/C][C]0.0121543978727244[/C][C]0.00607719893636221[/C][/ROW]
[ROW][C]29[/C][C]0.992663983882902[/C][C]0.0146720322341969[/C][C]0.00733601611709846[/C][/ROW]
[ROW][C]30[/C][C]0.994159836988509[/C][C]0.011680326022982[/C][C]0.00584016301149102[/C][/ROW]
[ROW][C]31[/C][C]0.99239070926903[/C][C]0.0152185814619404[/C][C]0.00760929073097018[/C][/ROW]
[ROW][C]32[/C][C]0.988960366277855[/C][C]0.0220792674442892[/C][C]0.0110396337221446[/C][/ROW]
[ROW][C]33[/C][C]0.990100356502774[/C][C]0.0197992869944518[/C][C]0.00989964349722591[/C][/ROW]
[ROW][C]34[/C][C]0.98632079844806[/C][C]0.0273584031038791[/C][C]0.0136792015519395[/C][/ROW]
[ROW][C]35[/C][C]0.980984904479454[/C][C]0.0380301910410923[/C][C]0.0190150955205462[/C][/ROW]
[ROW][C]36[/C][C]0.97461326797168[/C][C]0.0507734640566395[/C][C]0.0253867320283198[/C][/ROW]
[ROW][C]37[/C][C]0.974797636529453[/C][C]0.0504047269410947[/C][C]0.0252023634705473[/C][/ROW]
[ROW][C]38[/C][C]0.968893079204518[/C][C]0.0622138415909639[/C][C]0.0311069207954819[/C][/ROW]
[ROW][C]39[/C][C]0.971049671576997[/C][C]0.0579006568460069[/C][C]0.0289503284230035[/C][/ROW]
[ROW][C]40[/C][C]0.968200843584667[/C][C]0.0635983128306664[/C][C]0.0317991564153332[/C][/ROW]
[ROW][C]41[/C][C]0.957927108383638[/C][C]0.084145783232724[/C][C]0.042072891616362[/C][/ROW]
[ROW][C]42[/C][C]0.967256179630056[/C][C]0.0654876407398876[/C][C]0.0327438203699438[/C][/ROW]
[ROW][C]43[/C][C]0.957600081829373[/C][C]0.0847998363412539[/C][C]0.0423999181706269[/C][/ROW]
[ROW][C]44[/C][C]0.94968381337016[/C][C]0.100632373259679[/C][C]0.0503161866298397[/C][/ROW]
[ROW][C]45[/C][C]0.952315721828215[/C][C]0.0953685563435706[/C][C]0.0476842781717853[/C][/ROW]
[ROW][C]46[/C][C]0.956555070952271[/C][C]0.0868898580954587[/C][C]0.0434449290477294[/C][/ROW]
[ROW][C]47[/C][C]0.944647892199961[/C][C]0.110704215600078[/C][C]0.055352107800039[/C][/ROW]
[ROW][C]48[/C][C]0.933378323659924[/C][C]0.133243352680152[/C][C]0.0666216763400759[/C][/ROW]
[ROW][C]49[/C][C]0.973850109484612[/C][C]0.0522997810307755[/C][C]0.0261498905153878[/C][/ROW]
[ROW][C]50[/C][C]0.968957062888485[/C][C]0.0620858742230302[/C][C]0.0310429371115151[/C][/ROW]
[ROW][C]51[/C][C]0.964435265970101[/C][C]0.0711294680597976[/C][C]0.0355647340298988[/C][/ROW]
[ROW][C]52[/C][C]0.95599947347064[/C][C]0.0880010530587195[/C][C]0.0440005265293598[/C][/ROW]
[ROW][C]53[/C][C]0.94513716194923[/C][C]0.10972567610154[/C][C]0.0548628380507702[/C][/ROW]
[ROW][C]54[/C][C]0.934983518865746[/C][C]0.130032962268508[/C][C]0.0650164811342539[/C][/ROW]
[ROW][C]55[/C][C]0.937706571090041[/C][C]0.124586857819918[/C][C]0.0622934289099589[/C][/ROW]
[ROW][C]56[/C][C]0.928681681169874[/C][C]0.142636637660253[/C][C]0.0713183188301263[/C][/ROW]
[ROW][C]57[/C][C]0.938743730979267[/C][C]0.122512538041466[/C][C]0.061256269020733[/C][/ROW]
[ROW][C]58[/C][C]0.923690830912815[/C][C]0.15261833817437[/C][C]0.0763091690871852[/C][/ROW]
[ROW][C]59[/C][C]0.935182050326002[/C][C]0.129635899347995[/C][C]0.0648179496739976[/C][/ROW]
[ROW][C]60[/C][C]0.921109948557209[/C][C]0.157780102885582[/C][C]0.0788900514427912[/C][/ROW]
[ROW][C]61[/C][C]0.933972077840953[/C][C]0.132055844318094[/C][C]0.066027922159047[/C][/ROW]
[ROW][C]62[/C][C]0.921546130789095[/C][C]0.15690773842181[/C][C]0.0784538692109048[/C][/ROW]
[ROW][C]63[/C][C]0.93777619758796[/C][C]0.12444760482408[/C][C]0.0622238024120401[/C][/ROW]
[ROW][C]64[/C][C]0.928920454357311[/C][C]0.142159091285378[/C][C]0.0710795456426888[/C][/ROW]
[ROW][C]65[/C][C]0.925631894017057[/C][C]0.148736211965885[/C][C]0.0743681059829425[/C][/ROW]
[ROW][C]66[/C][C]0.970115135580862[/C][C]0.0597697288382766[/C][C]0.0298848644191383[/C][/ROW]
[ROW][C]67[/C][C]0.964364180567471[/C][C]0.0712716388650578[/C][C]0.0356358194325289[/C][/ROW]
[ROW][C]68[/C][C]0.966012247150212[/C][C]0.0679755056995754[/C][C]0.0339877528497877[/C][/ROW]
[ROW][C]69[/C][C]0.964891018057195[/C][C]0.0702179638856101[/C][C]0.0351089819428051[/C][/ROW]
[ROW][C]70[/C][C]0.965948157017577[/C][C]0.068103685964846[/C][C]0.034051842982423[/C][/ROW]
[ROW][C]71[/C][C]0.958798258507951[/C][C]0.0824034829840975[/C][C]0.0412017414920487[/C][/ROW]
[ROW][C]72[/C][C]0.97398671206807[/C][C]0.0520265758638599[/C][C]0.0260132879319299[/C][/ROW]
[ROW][C]73[/C][C]0.971085183233607[/C][C]0.0578296335327862[/C][C]0.0289148167663931[/C][/ROW]
[ROW][C]74[/C][C]0.964257184954312[/C][C]0.0714856300913762[/C][C]0.0357428150456881[/C][/ROW]
[ROW][C]75[/C][C]0.961910179134602[/C][C]0.0761796417307959[/C][C]0.038089820865398[/C][/ROW]
[ROW][C]76[/C][C]0.953057392707905[/C][C]0.0938852145841894[/C][C]0.0469426072920947[/C][/ROW]
[ROW][C]77[/C][C]0.971068803727479[/C][C]0.0578623925450415[/C][C]0.0289311962725207[/C][/ROW]
[ROW][C]78[/C][C]0.964572366555452[/C][C]0.0708552668890968[/C][C]0.0354276334445484[/C][/ROW]
[ROW][C]79[/C][C]0.959636221419054[/C][C]0.0807275571618925[/C][C]0.0403637785809463[/C][/ROW]
[ROW][C]80[/C][C]0.965755295507023[/C][C]0.068489408985954[/C][C]0.034244704492977[/C][/ROW]
[ROW][C]81[/C][C]0.957826957373046[/C][C]0.0843460852539084[/C][C]0.0421730426269542[/C][/ROW]
[ROW][C]82[/C][C]0.951070644053666[/C][C]0.0978587118926676[/C][C]0.0489293559463338[/C][/ROW]
[ROW][C]83[/C][C]0.941446804157151[/C][C]0.117106391685699[/C][C]0.0585531958428495[/C][/ROW]
[ROW][C]84[/C][C]0.939557206354822[/C][C]0.120885587290355[/C][C]0.0604427936451777[/C][/ROW]
[ROW][C]85[/C][C]0.927644825202128[/C][C]0.144710349595743[/C][C]0.0723551747978716[/C][/ROW]
[ROW][C]86[/C][C]0.911198436505408[/C][C]0.177603126989184[/C][C]0.088801563494592[/C][/ROW]
[ROW][C]87[/C][C]0.898838649260032[/C][C]0.202322701479937[/C][C]0.101161350739968[/C][/ROW]
[ROW][C]88[/C][C]0.878470558879803[/C][C]0.243058882240394[/C][C]0.121529441120197[/C][/ROW]
[ROW][C]89[/C][C]0.853903146825496[/C][C]0.292193706349008[/C][C]0.146096853174504[/C][/ROW]
[ROW][C]90[/C][C]0.894054896276326[/C][C]0.211890207447348[/C][C]0.105945103723674[/C][/ROW]
[ROW][C]91[/C][C]0.923118710593495[/C][C]0.15376257881301[/C][C]0.0768812894065049[/C][/ROW]
[ROW][C]92[/C][C]0.905337525903053[/C][C]0.189324948193893[/C][C]0.0946624740969465[/C][/ROW]
[ROW][C]93[/C][C]0.891697943665671[/C][C]0.216604112668659[/C][C]0.108302056334329[/C][/ROW]
[ROW][C]94[/C][C]0.874207133721673[/C][C]0.251585732556654[/C][C]0.125792866278327[/C][/ROW]
[ROW][C]95[/C][C]0.881122519303679[/C][C]0.237754961392643[/C][C]0.118877480696321[/C][/ROW]
[ROW][C]96[/C][C]0.867408873206239[/C][C]0.265182253587522[/C][C]0.132591126793761[/C][/ROW]
[ROW][C]97[/C][C]0.844502595838231[/C][C]0.310994808323538[/C][C]0.155497404161769[/C][/ROW]
[ROW][C]98[/C][C]0.821832760457777[/C][C]0.356334479084445[/C][C]0.178167239542223[/C][/ROW]
[ROW][C]99[/C][C]0.830499182754001[/C][C]0.339001634491998[/C][C]0.169500817245999[/C][/ROW]
[ROW][C]100[/C][C]0.800683133909045[/C][C]0.398633732181911[/C][C]0.199316866090955[/C][/ROW]
[ROW][C]101[/C][C]0.795813871600861[/C][C]0.408372256798277[/C][C]0.204186128399139[/C][/ROW]
[ROW][C]102[/C][C]0.765239413878399[/C][C]0.469521172243203[/C][C]0.234760586121601[/C][/ROW]
[ROW][C]103[/C][C]0.754369131366516[/C][C]0.491261737266969[/C][C]0.245630868633484[/C][/ROW]
[ROW][C]104[/C][C]0.849772736560882[/C][C]0.300454526878236[/C][C]0.150227263439118[/C][/ROW]
[ROW][C]105[/C][C]0.85896467422787[/C][C]0.282070651544259[/C][C]0.14103532577213[/C][/ROW]
[ROW][C]106[/C][C]0.876821736506131[/C][C]0.246356526987738[/C][C]0.123178263493869[/C][/ROW]
[ROW][C]107[/C][C]0.859593630931477[/C][C]0.280812738137046[/C][C]0.140406369068523[/C][/ROW]
[ROW][C]108[/C][C]0.868553618831469[/C][C]0.262892762337061[/C][C]0.131446381168531[/C][/ROW]
[ROW][C]109[/C][C]0.910589582026535[/C][C]0.178820835946931[/C][C]0.0894104179734655[/C][/ROW]
[ROW][C]110[/C][C]0.890269923389084[/C][C]0.219460153221832[/C][C]0.109730076610916[/C][/ROW]
[ROW][C]111[/C][C]0.878222494520362[/C][C]0.243555010959276[/C][C]0.121777505479638[/C][/ROW]
[ROW][C]112[/C][C]0.888213955755804[/C][C]0.223572088488391[/C][C]0.111786044244196[/C][/ROW]
[ROW][C]113[/C][C]0.89383942723785[/C][C]0.212321145524299[/C][C]0.10616057276215[/C][/ROW]
[ROW][C]114[/C][C]0.871561004659623[/C][C]0.256877990680754[/C][C]0.128438995340377[/C][/ROW]
[ROW][C]115[/C][C]0.922000392284613[/C][C]0.155999215430774[/C][C]0.0779996077153869[/C][/ROW]
[ROW][C]116[/C][C]0.904085021561346[/C][C]0.191829956877307[/C][C]0.0959149784386536[/C][/ROW]
[ROW][C]117[/C][C]0.880845330477098[/C][C]0.238309339045805[/C][C]0.119154669522902[/C][/ROW]
[ROW][C]118[/C][C]0.852612798532208[/C][C]0.294774402935584[/C][C]0.147387201467792[/C][/ROW]
[ROW][C]119[/C][C]0.850738561758183[/C][C]0.298522876483635[/C][C]0.149261438241817[/C][/ROW]
[ROW][C]120[/C][C]0.822705455381351[/C][C]0.354589089237299[/C][C]0.177294544618649[/C][/ROW]
[ROW][C]121[/C][C]0.802943792654645[/C][C]0.39411241469071[/C][C]0.197056207345355[/C][/ROW]
[ROW][C]122[/C][C]0.764063462474434[/C][C]0.471873075051131[/C][C]0.235936537525566[/C][/ROW]
[ROW][C]123[/C][C]0.720920176695385[/C][C]0.558159646609229[/C][C]0.279079823304615[/C][/ROW]
[ROW][C]124[/C][C]0.702339506801058[/C][C]0.595320986397885[/C][C]0.297660493198942[/C][/ROW]
[ROW][C]125[/C][C]0.654837789859388[/C][C]0.690324420281223[/C][C]0.345162210140611[/C][/ROW]
[ROW][C]126[/C][C]0.679312105377192[/C][C]0.641375789245616[/C][C]0.320687894622808[/C][/ROW]
[ROW][C]127[/C][C]0.632391509898461[/C][C]0.735216980203078[/C][C]0.367608490101539[/C][/ROW]
[ROW][C]128[/C][C]0.600094537847773[/C][C]0.799810924304454[/C][C]0.399905462152227[/C][/ROW]
[ROW][C]129[/C][C]0.754059305959485[/C][C]0.491881388081029[/C][C]0.245940694040515[/C][/ROW]
[ROW][C]130[/C][C]0.718160863557028[/C][C]0.563678272885944[/C][C]0.281839136442972[/C][/ROW]
[ROW][C]131[/C][C]0.717902964827487[/C][C]0.564194070345026[/C][C]0.282097035172513[/C][/ROW]
[ROW][C]132[/C][C]0.737538876156159[/C][C]0.524922247687683[/C][C]0.262461123843841[/C][/ROW]
[ROW][C]133[/C][C]0.69144699154871[/C][C]0.617106016902581[/C][C]0.30855300845129[/C][/ROW]
[ROW][C]134[/C][C]0.668625061940244[/C][C]0.662749876119512[/C][C]0.331374938059756[/C][/ROW]
[ROW][C]135[/C][C]0.611118195482257[/C][C]0.777763609035486[/C][C]0.388881804517743[/C][/ROW]
[ROW][C]136[/C][C]0.639628750587274[/C][C]0.720742498825451[/C][C]0.360371249412726[/C][/ROW]
[ROW][C]137[/C][C]0.625953314356307[/C][C]0.748093371287387[/C][C]0.374046685643693[/C][/ROW]
[ROW][C]138[/C][C]0.575278978290611[/C][C]0.849442043418777[/C][C]0.424721021709389[/C][/ROW]
[ROW][C]139[/C][C]0.532348951277772[/C][C]0.935302097444455[/C][C]0.467651048722228[/C][/ROW]
[ROW][C]140[/C][C]0.465083383119468[/C][C]0.930166766238937[/C][C]0.534916616880531[/C][/ROW]
[ROW][C]141[/C][C]0.422313371341597[/C][C]0.844626742683194[/C][C]0.577686628658403[/C][/ROW]
[ROW][C]142[/C][C]0.355189355622378[/C][C]0.710378711244755[/C][C]0.644810644377622[/C][/ROW]
[ROW][C]143[/C][C]0.416517931143702[/C][C]0.833035862287403[/C][C]0.583482068856298[/C][/ROW]
[ROW][C]144[/C][C]0.344369700065698[/C][C]0.688739400131395[/C][C]0.655630299934302[/C][/ROW]
[ROW][C]145[/C][C]0.295864230948808[/C][C]0.591728461897616[/C][C]0.704135769051192[/C][/ROW]
[ROW][C]146[/C][C]0.286495275364905[/C][C]0.572990550729811[/C][C]0.713504724635095[/C][/ROW]
[ROW][C]147[/C][C]0.363815044602544[/C][C]0.727630089205088[/C][C]0.636184955397456[/C][/ROW]
[ROW][C]148[/C][C]0.333514589758546[/C][C]0.667029179517091[/C][C]0.666485410241454[/C][/ROW]
[ROW][C]149[/C][C]0.338938866007399[/C][C]0.677877732014798[/C][C]0.661061133992601[/C][/ROW]
[ROW][C]150[/C][C]0.327106748442129[/C][C]0.654213496884258[/C][C]0.672893251557871[/C][/ROW]
[ROW][C]151[/C][C]0.299350096283865[/C][C]0.59870019256773[/C][C]0.700649903716135[/C][/ROW]
[ROW][C]152[/C][C]0.250874642055153[/C][C]0.501749284110306[/C][C]0.749125357944847[/C][/ROW]
[ROW][C]153[/C][C]0.197814050952882[/C][C]0.395628101905764[/C][C]0.802185949047118[/C][/ROW]
[ROW][C]154[/C][C]0.16144440171477[/C][C]0.322888803429539[/C][C]0.83855559828523[/C][/ROW]
[ROW][C]155[/C][C]0.20449008287777[/C][C]0.40898016575554[/C][C]0.79550991712223[/C][/ROW]
[ROW][C]156[/C][C]0.114992131646046[/C][C]0.229984263292091[/C][C]0.885007868353954[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200574&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200574&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9878179144294780.02436417114104330.0121820855705216
70.9787991010658260.04240179786834790.021200898934174
80.9699728673909680.06005426521806370.0300271326090319
90.9451414128013510.1097171743972980.0548585871986492
100.9096926908835090.1806146182329830.0903073091164913
110.8842852135866550.2314295728266890.115714786413345
120.8927724756811590.2144550486376820.107227524318841
130.9858387656509880.02832246869802360.0141612343490118
140.9806427775585070.03871444488298670.0193572224414933
150.9786910045923180.04261799081536430.0213089954076821
160.9716880460963550.05662390780729030.0283119539036451
170.961236458462650.07752708307470030.0387635415373502
180.9515805780155940.09683884396881160.0484194219844058
190.9479521105473080.1040957789053840.0520478894526919
200.934134687445840.131730625108320.0658653125541601
210.9190937815938380.1618124368123250.0809062184061624
220.956794040738240.08641191852352070.0432059592617603
230.9426164888757550.114767022248490.0573835111242452
240.9384109537129280.1231780925741450.0615890462870724
250.9401178777215990.1197642445568030.0598821222784013
260.9962388910844360.007522217831128590.0037611089155643
270.9956521719813480.008695656037303520.00434782801865176
280.9939228010636380.01215439787272440.00607719893636221
290.9926639838829020.01467203223419690.00733601611709846
300.9941598369885090.0116803260229820.00584016301149102
310.992390709269030.01521858146194040.00760929073097018
320.9889603662778550.02207926744428920.0110396337221446
330.9901003565027740.01979928699445180.00989964349722591
340.986320798448060.02735840310387910.0136792015519395
350.9809849044794540.03803019104109230.0190150955205462
360.974613267971680.05077346405663950.0253867320283198
370.9747976365294530.05040472694109470.0252023634705473
380.9688930792045180.06221384159096390.0311069207954819
390.9710496715769970.05790065684600690.0289503284230035
400.9682008435846670.06359831283066640.0317991564153332
410.9579271083836380.0841457832327240.042072891616362
420.9672561796300560.06548764073988760.0327438203699438
430.9576000818293730.08479983634125390.0423999181706269
440.949683813370160.1006323732596790.0503161866298397
450.9523157218282150.09536855634357060.0476842781717853
460.9565550709522710.08688985809545870.0434449290477294
470.9446478921999610.1107042156000780.055352107800039
480.9333783236599240.1332433526801520.0666216763400759
490.9738501094846120.05229978103077550.0261498905153878
500.9689570628884850.06208587422303020.0310429371115151
510.9644352659701010.07112946805979760.0355647340298988
520.955999473470640.08800105305871950.0440005265293598
530.945137161949230.109725676101540.0548628380507702
540.9349835188657460.1300329622685080.0650164811342539
550.9377065710900410.1245868578199180.0622934289099589
560.9286816811698740.1426366376602530.0713183188301263
570.9387437309792670.1225125380414660.061256269020733
580.9236908309128150.152618338174370.0763091690871852
590.9351820503260020.1296358993479950.0648179496739976
600.9211099485572090.1577801028855820.0788900514427912
610.9339720778409530.1320558443180940.066027922159047
620.9215461307890950.156907738421810.0784538692109048
630.937776197587960.124447604824080.0622238024120401
640.9289204543573110.1421590912853780.0710795456426888
650.9256318940170570.1487362119658850.0743681059829425
660.9701151355808620.05976972883827660.0298848644191383
670.9643641805674710.07127163886505780.0356358194325289
680.9660122471502120.06797550569957540.0339877528497877
690.9648910180571950.07021796388561010.0351089819428051
700.9659481570175770.0681036859648460.034051842982423
710.9587982585079510.08240348298409750.0412017414920487
720.973986712068070.05202657586385990.0260132879319299
730.9710851832336070.05782963353278620.0289148167663931
740.9642571849543120.07148563009137620.0357428150456881
750.9619101791346020.07617964173079590.038089820865398
760.9530573927079050.09388521458418940.0469426072920947
770.9710688037274790.05786239254504150.0289311962725207
780.9645723665554520.07085526688909680.0354276334445484
790.9596362214190540.08072755716189250.0403637785809463
800.9657552955070230.0684894089859540.034244704492977
810.9578269573730460.08434608525390840.0421730426269542
820.9510706440536660.09785871189266760.0489293559463338
830.9414468041571510.1171063916856990.0585531958428495
840.9395572063548220.1208855872903550.0604427936451777
850.9276448252021280.1447103495957430.0723551747978716
860.9111984365054080.1776031269891840.088801563494592
870.8988386492600320.2023227014799370.101161350739968
880.8784705588798030.2430588822403940.121529441120197
890.8539031468254960.2921937063490080.146096853174504
900.8940548962763260.2118902074473480.105945103723674
910.9231187105934950.153762578813010.0768812894065049
920.9053375259030530.1893249481938930.0946624740969465
930.8916979436656710.2166041126686590.108302056334329
940.8742071337216730.2515857325566540.125792866278327
950.8811225193036790.2377549613926430.118877480696321
960.8674088732062390.2651822535875220.132591126793761
970.8445025958382310.3109948083235380.155497404161769
980.8218327604577770.3563344790844450.178167239542223
990.8304991827540010.3390016344919980.169500817245999
1000.8006831339090450.3986337321819110.199316866090955
1010.7958138716008610.4083722567982770.204186128399139
1020.7652394138783990.4695211722432030.234760586121601
1030.7543691313665160.4912617372669690.245630868633484
1040.8497727365608820.3004545268782360.150227263439118
1050.858964674227870.2820706515442590.14103532577213
1060.8768217365061310.2463565269877380.123178263493869
1070.8595936309314770.2808127381370460.140406369068523
1080.8685536188314690.2628927623370610.131446381168531
1090.9105895820265350.1788208359469310.0894104179734655
1100.8902699233890840.2194601532218320.109730076610916
1110.8782224945203620.2435550109592760.121777505479638
1120.8882139557558040.2235720884883910.111786044244196
1130.893839427237850.2123211455242990.10616057276215
1140.8715610046596230.2568779906807540.128438995340377
1150.9220003922846130.1559992154307740.0779996077153869
1160.9040850215613460.1918299568773070.0959149784386536
1170.8808453304770980.2383093390458050.119154669522902
1180.8526127985322080.2947744029355840.147387201467792
1190.8507385617581830.2985228764836350.149261438241817
1200.8227054553813510.3545890892372990.177294544618649
1210.8029437926546450.394112414690710.197056207345355
1220.7640634624744340.4718730750511310.235936537525566
1230.7209201766953850.5581596466092290.279079823304615
1240.7023395068010580.5953209863978850.297660493198942
1250.6548377898593880.6903244202812230.345162210140611
1260.6793121053771920.6413757892456160.320687894622808
1270.6323915098984610.7352169802030780.367608490101539
1280.6000945378477730.7998109243044540.399905462152227
1290.7540593059594850.4918813880810290.245940694040515
1300.7181608635570280.5636782728859440.281839136442972
1310.7179029648274870.5641940703450260.282097035172513
1320.7375388761561590.5249222476876830.262461123843841
1330.691446991548710.6171060169025810.30855300845129
1340.6686250619402440.6627498761195120.331374938059756
1350.6111181954822570.7777636090354860.388881804517743
1360.6396287505872740.7207424988254510.360371249412726
1370.6259533143563070.7480933712873870.374046685643693
1380.5752789782906110.8494420434187770.424721021709389
1390.5323489512777720.9353020974444550.467651048722228
1400.4650833831194680.9301667662389370.534916616880531
1410.4223133713415970.8446267426831940.577686628658403
1420.3551893556223780.7103787112447550.644810644377622
1430.4165179311437020.8330358622874030.583482068856298
1440.3443697000656980.6887394001313950.655630299934302
1450.2958642309488080.5917284618976160.704135769051192
1460.2864952753649050.5729905507298110.713504724635095
1470.3638150446025440.7276300892050880.636184955397456
1480.3335145897585460.6670291795170910.666485410241454
1490.3389388660073990.6778777320147980.661061133992601
1500.3271067484421290.6542134968842580.672893251557871
1510.2993500962838650.598700192567730.700649903716135
1520.2508746420551530.5017492841103060.749125357944847
1530.1978140509528820.3956281019057640.802185949047118
1540.161444401714770.3228888034295390.83855559828523
1550.204490082877770.408980165755540.79550991712223
1560.1149921316460460.2299842632920910.885007868353954







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0132450331125828NOK
5% type I error level150.0993377483443709NOK
10% type I error level510.337748344370861NOK

\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 & 2 & 0.0132450331125828 & NOK \tabularnewline
5% type I error level & 15 & 0.0993377483443709 & NOK \tabularnewline
10% type I error level & 51 & 0.337748344370861 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200574&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]2[/C][C]0.0132450331125828[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.0993377483443709[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]51[/C][C]0.337748344370861[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200574&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200574&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0132450331125828NOK
5% type I error level150.0993377483443709NOK
10% type I error level510.337748344370861NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}