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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 15 Dec 2011 14:15:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/15/t1323976525vzetq102pnhbg7c.htm/, Retrieved Wed, 08 May 2024 23:26:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155658, Retrieved Wed, 08 May 2024 23:26:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 20:18:32] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [pearson correlati...] [2011-12-15 19:04:01] [1ed874da5cc4aa1cd1ced057f766d90b]
- RMP       [Multiple Regression] [PLC] [2011-12-15 19:15:06] [452d9c400285ceb08a690c4b81b76477] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14
2	5	39	32	16	11	18
2	5	30	35	19	15	11
1	5	31	33	15	6	12
2	8	34	37	14	13	16
2	6	35	29	13	10	18
2	5	39	31	19	12	14
2	6	34	36	15	14	14
2	5	36	35	14	12	15
2	4	37	38	15	6	15
1	6	38	31	16	10	17
2	5	36	34	16	12	19
1	5	38	35	16	12	10
2	6	39	38	16	11	16
2	7	33	37	17	15	18
1	6	32	33	15	12	14
1	7	36	32	15	10	14
2	6	38	38	20	12	17
1	8	39	38	18	11	14
2	7	32	32	16	12	16
1	5	32	33	16	11	18
2	5	31	31	16	12	11
2	7	39	38	19	13	14
2	7	37	39	16	11	12
1	5	39	32	17	9	17
2	4	41	32	17	13	9
1	10	36	35	16	10	16
2	6	33	37	15	14	14
2	5	33	33	16	12	15
1	5	34	33	14	10	11
2	5	31	28	15	12	16
1	5	27	32	12	8	13
2	6	37	31	14	10	17
2	5	34	37	16	12	15
1	5	34	30	14	12	14
1	5	32	33	7	7	16
1	5	29	31	10	6	9
1	5	36	33	14	12	15
2	5	29	31	16	10	17
1	5	35	33	16	10	13
1	5	37	32	16	10	15
2	7	34	33	14	12	16
1	5	38	32	20	15	16
1	6	35	33	14	10	12
2	7	38	28	14	10	12
2	7	37	35	11	12	11
2	5	38	39	14	13	15
2	5	33	34	15	11	15
2	4	36	38	16	11	17
1	5	38	32	14	12	13
2	4	32	38	16	14	16
1	5	32	30	14	10	14
1	5	32	33	12	12	11
2	7	34	38	16	13	12
1	5	32	32	9	5	12
2	5	37	32	14	6	15
2	6	39	34	16	12	16
2	4	29	34	16	12	15
1	6	37	36	15	11	12
2	6	35	34	16	10	12
1	5	30	28	12	7	8
1	7	38	34	16	12	13
2	6	34	35	16	14	11
2	8	31	35	14	11	14
2	7	34	31	16	12	15
1	5	35	37	17	13	10
2	6	36	35	18	14	11
1	6	30	27	18	11	12
2	5	39	40	12	12	15
1	5	35	37	16	12	15
1	5	38	36	10	8	14
2	5	31	38	14	11	16
2	4	34	39	18	14	15
1	6	38	41	18	14	15
1	6	34	27	16	12	13
2	6	39	30	17	9	12
2	6	37	37	16	13	17
2	7	34	31	16	11	13
1	5	28	31	13	12	15
1	7	37	27	16	12	13
1	6	33	36	16	12	15
1	5	37	38	20	12	16
2	5	35	37	16	12	15
1	4	37	33	15	12	16
2	8	32	34	15	11	15
2	8	33	31	16	10	14
1	5	38	39	14	9	15
2	5	33	34	16	12	14
2	6	29	32	16	12	13
2	4	33	33	15	12	7
2	5	31	36	12	9	17
2	5	36	32	17	15	13
2	5	35	41	16	12	15
2	5	32	28	15	12	14
2	6	29	30	13	12	13
2	6	39	36	16	10	16
2	5	37	35	16	13	12
2	6	35	31	16	9	14
1	5	37	34	16	12	17
1	7	32	36	14	10	15
2	5	38	36	16	14	17
1	6	37	35	16	11	12
2	6	36	37	20	15	16
1	6	32	28	15	11	11
2	4	33	39	16	11	15
1	5	40	32	13	12	9
2	5	38	35	17	12	16
1	7	41	39	16	12	15
1	6	36	35	16	11	10
2	9	43	42	12	7	10
2	6	30	34	16	12	15
2	6	31	33	16	14	11
2	5	32	41	17	11	13
1	6	32	33	13	11	14
2	5	37	34	12	10	18
1	8	37	32	18	13	16
2	7	33	40	14	13	14
2	5	34	40	14	8	14
2	7	33	35	13	11	14
2	6	38	36	16	12	14
2	6	33	37	13	11	12
2	9	31	27	16	13	14
2	7	38	39	13	12	15
2	6	37	38	16	14	15
2	5	33	31	15	13	15
2	5	31	33	16	15	13
1	6	39	32	15	10	17
2	6	44	39	17	11	17
2	7	33	36	15	9	19
2	5	35	33	12	11	15
1	5	32	33	16	10	13
1	5	28	32	10	11	9
2	6	40	37	16	8	15
1	4	27	30	12	11	15
1	5	37	38	14	12	15
2	7	32	29	15	12	16
1	5	28	22	13	9	11
1	7	34	35	15	11	14
2	7	30	35	11	10	11
2	6	35	34	12	8	15
1	5	31	35	8	9	13
2	8	32	34	16	8	15
1	5	30	34	15	9	16
2	5	30	35	17	15	14
1	5	31	23	16	11	15
2	6	40	31	10	8	16
2	4	32	27	18	13	16
1	5	36	36	13	12	11
1	5	32	31	16	12	12
1	7	35	32	13	9	9
2	6	38	39	10	7	16
2	7	42	37	15	13	13
1	10	34	38	16	9	16
2	6	35	39	16	6	12
2	8	35	34	14	8	9
2	4	33	31	10	8	13
2	5	36	32	17	15	13
2	6	32	37	13	6	14
2	7	33	36	15	9	19
2	7	34	32	16	11	13
2	6	32	35	12	8	12
2	6	34	36	13	8	13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155658&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155658&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155658&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
learning[t] = + 3.40846051542247 + 0.0213994668732461gender[t] + 0.116267086993093age[t] + 0.114045929233412connected[t] -0.0291093372979854separate[t] + 0.560139838484923software[t] + 0.120061394553405happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
learning[t] =  +  3.40846051542247 +  0.0213994668732461gender[t] +  0.116267086993093age[t] +  0.114045929233412connected[t] -0.0291093372979854separate[t] +  0.560139838484923software[t] +  0.120061394553405happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155658&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]learning[t] =  +  3.40846051542247 +  0.0213994668732461gender[t] +  0.116267086993093age[t] +  0.114045929233412connected[t] -0.0291093372979854separate[t] +  0.560139838484923software[t] +  0.120061394553405happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155658&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155658&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
learning[t] = + 3.40846051542247 + 0.0213994668732461gender[t] + 0.116267086993093age[t] + 0.114045929233412connected[t] -0.0291093372979854separate[t] + 0.560139838484923software[t] + 0.120061394553405happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.408460515422472.0108971.6950.0920840.046042
gender0.02139946687324610.3179570.06730.9464270.473214
age0.1162670869930930.1282580.90650.3660740.183037
connected0.1140459292334120.0472412.41410.0169380.008469
separate-0.02910933729798540.045679-0.63730.5249010.26245
software0.5601398384849230.0695838.0500
happiness0.1200613945534050.0644011.86430.0641760.032088

\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) & 3.40846051542247 & 2.010897 & 1.695 & 0.092084 & 0.046042 \tabularnewline
gender & 0.0213994668732461 & 0.317957 & 0.0673 & 0.946427 & 0.473214 \tabularnewline
age & 0.116267086993093 & 0.128258 & 0.9065 & 0.366074 & 0.183037 \tabularnewline
connected & 0.114045929233412 & 0.047241 & 2.4141 & 0.016938 & 0.008469 \tabularnewline
separate & -0.0291093372979854 & 0.045679 & -0.6373 & 0.524901 & 0.26245 \tabularnewline
software & 0.560139838484923 & 0.069583 & 8.05 & 0 & 0 \tabularnewline
happiness & 0.120061394553405 & 0.064401 & 1.8643 & 0.064176 & 0.032088 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155658&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]3.40846051542247[/C][C]2.010897[/C][C]1.695[/C][C]0.092084[/C][C]0.046042[/C][/ROW]
[ROW][C]gender[/C][C]0.0213994668732461[/C][C]0.317957[/C][C]0.0673[/C][C]0.946427[/C][C]0.473214[/C][/ROW]
[ROW][C]age[/C][C]0.116267086993093[/C][C]0.128258[/C][C]0.9065[/C][C]0.366074[/C][C]0.183037[/C][/ROW]
[ROW][C]connected[/C][C]0.114045929233412[/C][C]0.047241[/C][C]2.4141[/C][C]0.016938[/C][C]0.008469[/C][/ROW]
[ROW][C]separate[/C][C]-0.0291093372979854[/C][C]0.045679[/C][C]-0.6373[/C][C]0.524901[/C][C]0.26245[/C][/ROW]
[ROW][C]software[/C][C]0.560139838484923[/C][C]0.069583[/C][C]8.05[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]happiness[/C][C]0.120061394553405[/C][C]0.064401[/C][C]1.8643[/C][C]0.064176[/C][C]0.032088[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155658&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155658&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.408460515422472.0108971.6950.0920840.046042
gender0.02139946687324610.3179570.06730.9464270.473214
age0.1162670869930930.1282580.90650.3660740.183037
connected0.1140459292334120.0472412.41410.0169380.008469
separate-0.02910933729798540.045679-0.63730.5249010.26245
software0.5601398384849230.0695838.0500
happiness0.1200613945534050.0644011.86430.0641760.032088






Multiple Linear Regression - Regression Statistics
Multiple R0.590934825685492
R-squared0.349203968207943
Adjusted R-squared0.324011863751476
F-TEST (value)13.8616433895543
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.38977718222577e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85506393501564
Sum Squared Residuals533.395641464334

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.590934825685492 \tabularnewline
R-squared & 0.349203968207943 \tabularnewline
Adjusted R-squared & 0.324011863751476 \tabularnewline
F-TEST (value) & 13.8616433895543 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.38977718222577e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85506393501564 \tabularnewline
Sum Squared Residuals & 533.395641464334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155658&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.590934825685492[/C][/ROW]
[ROW][C]R-squared[/C][C]0.349203968207943[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324011863751476[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.8616433895543[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]1.38977718222577e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85506393501564[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]533.395641464334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155658&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155658&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.590934825685492
R-squared0.349203968207943
Adjusted R-squared0.324011863751476
F-TEST (value)13.8616433895543
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.38977718222577e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85506393501564
Sum Squared Residuals533.395641464334






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2373949249338-3.23739492493382
21615.87153065599740.128469344002595
31916.15791887306862.8420811269314
41511.38758685821383.61241314178617
51416.3847124721827-2.38471247218274
61315.0588021994659-2.05880219946589
71915.98053425356673.0194657464333
81516.5013046848726-1.50130468487265
91415.6420205112279-1.64202051122792
101512.19163231066472.80836768933525
111615.20126045114350.798739548856502
121616.1513754267395-0.151375426739527
131615.24840593005450.751594069945524
141615.57301893009580.426981069904223
151717.5148019220329-0.514801922032887
161515.2188616944567-0.218861694456688
171514.70014215871160.299857841288432
182016.13917423390073.86082576609931
191815.54403084810192.45596915189809
201615.62576037472780.374239625272177
211615.02270034719230.977299652807709
221614.70798263603921.29201736396081
231916.56944290495192.43055709504809
241614.95183924311041.04816075688956
251714.60979011760092.39020988239909
261716.02308255346030.97691744653966
271615.20173819690370.798261803096298
281516.3581494183412-1.35814941834125
291615.35810139812370.641898601876343
301413.85022260530040.149777394699642
311515.3956176207002-0.395617620700165
321212.2008535501014-0.200853550101422
331415.1086139887833-1.10861398878333
341615.35570997816510.644290021834872
351415.4180144778244-1.41801447782437
36712.5420182041458-5.54201820414579
371010.8575294906828-0.857529490682765
381415.6788397189506-1.67883971895065
391614.07997946792291.92002053207706
401614.20439132364061.79560867635942
411614.70171530851221.2982846914878
421415.8247428958967-1.82474289589666
432017.73652182472362.26347817527637
441414.2005970160803-0.200597016080268
451414.8259480441368-0.82594804413677
461115.5083550362339-4.5083550362339
471416.3138148589877-2.31381485898773
481514.76885222234070.231147777659251
491615.11840836296280.881591637037241
501415.6959181256086-1.69591812560865
511616.2225827669305-0.222582766930476
521414.0696429423877-0.0696429423877041
531214.7424104238034-2.74241042380338
541615.7590904696780.240909530321962
55910.9706022862603-1.97060228626031
561412.48255542144581.51744457855425
571616.2495961177726-0.249596117772642
581614.75654125689891.24345874310107
591514.90150070113810.0984992988619412
601614.19288714565551.80711285434447
611211.49898187574170.501018124258347
621615.87023362499890.129766375001137
631616.1702298385104-0.170229838510419
641414.7403908930018-0.740390893001815
651615.76290017593920.237099824060773
661715.40818930624321.59181069375681
671816.39832169697721.60167830302276
681814.3651632321863.63483676781396
691215.8386116124382-3.83861161243823
701615.44835644052530.551643559474706
711013.4589828170304-3.45898281703042
721414.5443844092354-0.544384409235388
731816.30150389354591.69849610645409
741816.91060364299651.08939635700347
751615.5015481821580.498451817841981
761714.20536837329622.7946316267038
771616.6143774804502-0.61437748045019
781614.96263754834751.03736245165251
791314.8246909596793-1.82469095967932
801615.95995305685130.0400469431486512
811615.36564100634950.634358993650451
822015.76740035624754.23259964375246
831615.46975590739850.53024409260146
841515.7966799557444-0.79667995574437
851515.0036075540866-0.0036075540866172
861614.52478026217571.47521973782434
871414.0518560381748-0.051856038174789
881615.20893066627230.791069333727733
891614.80717131637431.19282868362572
901514.28134315470330.718656845296674
911213.6023848014149-1.60238480141492
921717.1696452494698-0.169645249469839
931615.35331855820660.646681441793402
941515.2695407608268-0.269540760826768
951314.8653899909703-1.86538999097025
961615.07109776620680.928902233793175
971615.9560220952860.0439779047139567
981613.96019810817142.03980189182863
991616.0038990999929-0.00389909999288316
1001414.2475824871394-0.247582487139383
1011617.2014054984734-1.20140549847342
1021614.9306100384361.06938996156396
1032017.50054983363322.49945016636678
1041514.44408435880150.555915641198523
1051614.50703844885771.49296155114227
1061315.4437644058619-2.44376440586185
1071715.99017376424821.00982623575185
1081616.306947515316-0.306947515315981
1091614.57644132009581.42355867990418
1101213.3006388375566-1.30063883755664
1111615.10312136011850.896878639881471
1121615.88631072540620.113689274593845
1131714.21091814291462.78908185708537
1141314.6587218559718-1.65872185597177
1151215.0250802844497-3.02508028444969
1161816.85099747949971.14900252050035
1171415.8269486549555-1.82694865495546
1181412.90776121777811.09223878222193
1191314.8522156644755-1.85221566447555
1201615.83720872483640.16279127516355
1211314.4376071137797-1.43760711377967
1221616.2098123553486-0.209812355348638
1231315.986209194489-2.98620919448899
1241616.9052851925303-0.905285192530318
1251515.9764599112046-0.976459911204551
1261616.5703062660048-0.570306266004794
1271515.2861970430789-0.286197043078925
1281716.23420063351830.765799366481744
1291514.30313362297470.696866377025263
1301215.0260534181056-3.02605341810556
1311613.86225353594032.13774646405966
1321013.515073416576-3.51507341657598
1331613.9156932866192.084306713381
1341214.0633474422659-2.06334744226588
1351415.6473389616941-1.64733896169413
1361515.7130883866218-0.713088386621779
1371312.92600990169280.0739900983071978
1381514.94486212683570.0551378731642888
1391113.5897538546302-2.58975385463017
1401213.4327916523459-1.4327916523459
141813.129849093626-5.12984909362604
1421613.32318803863182.67681196136815
1431513.40509668535081.59490331464918
1441716.51810305672880.481896943271184
1451614.83956360727851.16043639272148
1461014.2104107049603-4.21041070496032
1471815.98264563872342.01735436127661
1481315.1112661288431-2.11126612884307
1491614.92069049295281.07930950704724
1501313.4256494182262-0.42564941822621
1511013.1893043096247-3.18930430962469
1521516.8206286353967-1.82062863539672
1531614.3261784880581.673821511942
1541611.80678110522594.19321889477409
1551412.94495745901171.05504254098835
1561012.81937084268-2.81937084268003
1571717.1696452494698-0.169645249469839
1581311.76298478122851.23701521877155
1591514.30313362297470.696866377025263
1601614.93352821104951.06647178895049
1611212.7013603436875-0.701360343687461
1621313.0204042594097-0.0204042594097041

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.2373949249338 & -3.23739492493382 \tabularnewline
2 & 16 & 15.8715306559974 & 0.128469344002595 \tabularnewline
3 & 19 & 16.1579188730686 & 2.8420811269314 \tabularnewline
4 & 15 & 11.3875868582138 & 3.61241314178617 \tabularnewline
5 & 14 & 16.3847124721827 & -2.38471247218274 \tabularnewline
6 & 13 & 15.0588021994659 & -2.05880219946589 \tabularnewline
7 & 19 & 15.9805342535667 & 3.0194657464333 \tabularnewline
8 & 15 & 16.5013046848726 & -1.50130468487265 \tabularnewline
9 & 14 & 15.6420205112279 & -1.64202051122792 \tabularnewline
10 & 15 & 12.1916323106647 & 2.80836768933525 \tabularnewline
11 & 16 & 15.2012604511435 & 0.798739548856502 \tabularnewline
12 & 16 & 16.1513754267395 & -0.151375426739527 \tabularnewline
13 & 16 & 15.2484059300545 & 0.751594069945524 \tabularnewline
14 & 16 & 15.5730189300958 & 0.426981069904223 \tabularnewline
15 & 17 & 17.5148019220329 & -0.514801922032887 \tabularnewline
16 & 15 & 15.2188616944567 & -0.218861694456688 \tabularnewline
17 & 15 & 14.7001421587116 & 0.299857841288432 \tabularnewline
18 & 20 & 16.1391742339007 & 3.86082576609931 \tabularnewline
19 & 18 & 15.5440308481019 & 2.45596915189809 \tabularnewline
20 & 16 & 15.6257603747278 & 0.374239625272177 \tabularnewline
21 & 16 & 15.0227003471923 & 0.977299652807709 \tabularnewline
22 & 16 & 14.7079826360392 & 1.29201736396081 \tabularnewline
23 & 19 & 16.5694429049519 & 2.43055709504809 \tabularnewline
24 & 16 & 14.9518392431104 & 1.04816075688956 \tabularnewline
25 & 17 & 14.6097901176009 & 2.39020988239909 \tabularnewline
26 & 17 & 16.0230825534603 & 0.97691744653966 \tabularnewline
27 & 16 & 15.2017381969037 & 0.798261803096298 \tabularnewline
28 & 15 & 16.3581494183412 & -1.35814941834125 \tabularnewline
29 & 16 & 15.3581013981237 & 0.641898601876343 \tabularnewline
30 & 14 & 13.8502226053004 & 0.149777394699642 \tabularnewline
31 & 15 & 15.3956176207002 & -0.395617620700165 \tabularnewline
32 & 12 & 12.2008535501014 & -0.200853550101422 \tabularnewline
33 & 14 & 15.1086139887833 & -1.10861398878333 \tabularnewline
34 & 16 & 15.3557099781651 & 0.644290021834872 \tabularnewline
35 & 14 & 15.4180144778244 & -1.41801447782437 \tabularnewline
36 & 7 & 12.5420182041458 & -5.54201820414579 \tabularnewline
37 & 10 & 10.8575294906828 & -0.857529490682765 \tabularnewline
38 & 14 & 15.6788397189506 & -1.67883971895065 \tabularnewline
39 & 16 & 14.0799794679229 & 1.92002053207706 \tabularnewline
40 & 16 & 14.2043913236406 & 1.79560867635942 \tabularnewline
41 & 16 & 14.7017153085122 & 1.2982846914878 \tabularnewline
42 & 14 & 15.8247428958967 & -1.82474289589666 \tabularnewline
43 & 20 & 17.7365218247236 & 2.26347817527637 \tabularnewline
44 & 14 & 14.2005970160803 & -0.200597016080268 \tabularnewline
45 & 14 & 14.8259480441368 & -0.82594804413677 \tabularnewline
46 & 11 & 15.5083550362339 & -4.5083550362339 \tabularnewline
47 & 14 & 16.3138148589877 & -2.31381485898773 \tabularnewline
48 & 15 & 14.7688522223407 & 0.231147777659251 \tabularnewline
49 & 16 & 15.1184083629628 & 0.881591637037241 \tabularnewline
50 & 14 & 15.6959181256086 & -1.69591812560865 \tabularnewline
51 & 16 & 16.2225827669305 & -0.222582766930476 \tabularnewline
52 & 14 & 14.0696429423877 & -0.0696429423877041 \tabularnewline
53 & 12 & 14.7424104238034 & -2.74241042380338 \tabularnewline
54 & 16 & 15.759090469678 & 0.240909530321962 \tabularnewline
55 & 9 & 10.9706022862603 & -1.97060228626031 \tabularnewline
56 & 14 & 12.4825554214458 & 1.51744457855425 \tabularnewline
57 & 16 & 16.2495961177726 & -0.249596117772642 \tabularnewline
58 & 16 & 14.7565412568989 & 1.24345874310107 \tabularnewline
59 & 15 & 14.9015007011381 & 0.0984992988619412 \tabularnewline
60 & 16 & 14.1928871456555 & 1.80711285434447 \tabularnewline
61 & 12 & 11.4989818757417 & 0.501018124258347 \tabularnewline
62 & 16 & 15.8702336249989 & 0.129766375001137 \tabularnewline
63 & 16 & 16.1702298385104 & -0.170229838510419 \tabularnewline
64 & 14 & 14.7403908930018 & -0.740390893001815 \tabularnewline
65 & 16 & 15.7629001759392 & 0.237099824060773 \tabularnewline
66 & 17 & 15.4081893062432 & 1.59181069375681 \tabularnewline
67 & 18 & 16.3983216969772 & 1.60167830302276 \tabularnewline
68 & 18 & 14.365163232186 & 3.63483676781396 \tabularnewline
69 & 12 & 15.8386116124382 & -3.83861161243823 \tabularnewline
70 & 16 & 15.4483564405253 & 0.551643559474706 \tabularnewline
71 & 10 & 13.4589828170304 & -3.45898281703042 \tabularnewline
72 & 14 & 14.5443844092354 & -0.544384409235388 \tabularnewline
73 & 18 & 16.3015038935459 & 1.69849610645409 \tabularnewline
74 & 18 & 16.9106036429965 & 1.08939635700347 \tabularnewline
75 & 16 & 15.501548182158 & 0.498451817841981 \tabularnewline
76 & 17 & 14.2053683732962 & 2.7946316267038 \tabularnewline
77 & 16 & 16.6143774804502 & -0.61437748045019 \tabularnewline
78 & 16 & 14.9626375483475 & 1.03736245165251 \tabularnewline
79 & 13 & 14.8246909596793 & -1.82469095967932 \tabularnewline
80 & 16 & 15.9599530568513 & 0.0400469431486512 \tabularnewline
81 & 16 & 15.3656410063495 & 0.634358993650451 \tabularnewline
82 & 20 & 15.7674003562475 & 4.23259964375246 \tabularnewline
83 & 16 & 15.4697559073985 & 0.53024409260146 \tabularnewline
84 & 15 & 15.7966799557444 & -0.79667995574437 \tabularnewline
85 & 15 & 15.0036075540866 & -0.0036075540866172 \tabularnewline
86 & 16 & 14.5247802621757 & 1.47521973782434 \tabularnewline
87 & 14 & 14.0518560381748 & -0.051856038174789 \tabularnewline
88 & 16 & 15.2089306662723 & 0.791069333727733 \tabularnewline
89 & 16 & 14.8071713163743 & 1.19282868362572 \tabularnewline
90 & 15 & 14.2813431547033 & 0.718656845296674 \tabularnewline
91 & 12 & 13.6023848014149 & -1.60238480141492 \tabularnewline
92 & 17 & 17.1696452494698 & -0.169645249469839 \tabularnewline
93 & 16 & 15.3533185582066 & 0.646681441793402 \tabularnewline
94 & 15 & 15.2695407608268 & -0.269540760826768 \tabularnewline
95 & 13 & 14.8653899909703 & -1.86538999097025 \tabularnewline
96 & 16 & 15.0710977662068 & 0.928902233793175 \tabularnewline
97 & 16 & 15.956022095286 & 0.0439779047139567 \tabularnewline
98 & 16 & 13.9601981081714 & 2.03980189182863 \tabularnewline
99 & 16 & 16.0038990999929 & -0.00389909999288316 \tabularnewline
100 & 14 & 14.2475824871394 & -0.247582487139383 \tabularnewline
101 & 16 & 17.2014054984734 & -1.20140549847342 \tabularnewline
102 & 16 & 14.930610038436 & 1.06938996156396 \tabularnewline
103 & 20 & 17.5005498336332 & 2.49945016636678 \tabularnewline
104 & 15 & 14.4440843588015 & 0.555915641198523 \tabularnewline
105 & 16 & 14.5070384488577 & 1.49296155114227 \tabularnewline
106 & 13 & 15.4437644058619 & -2.44376440586185 \tabularnewline
107 & 17 & 15.9901737642482 & 1.00982623575185 \tabularnewline
108 & 16 & 16.306947515316 & -0.306947515315981 \tabularnewline
109 & 16 & 14.5764413200958 & 1.42355867990418 \tabularnewline
110 & 12 & 13.3006388375566 & -1.30063883755664 \tabularnewline
111 & 16 & 15.1031213601185 & 0.896878639881471 \tabularnewline
112 & 16 & 15.8863107254062 & 0.113689274593845 \tabularnewline
113 & 17 & 14.2109181429146 & 2.78908185708537 \tabularnewline
114 & 13 & 14.6587218559718 & -1.65872185597177 \tabularnewline
115 & 12 & 15.0250802844497 & -3.02508028444969 \tabularnewline
116 & 18 & 16.8509974794997 & 1.14900252050035 \tabularnewline
117 & 14 & 15.8269486549555 & -1.82694865495546 \tabularnewline
118 & 14 & 12.9077612177781 & 1.09223878222193 \tabularnewline
119 & 13 & 14.8522156644755 & -1.85221566447555 \tabularnewline
120 & 16 & 15.8372087248364 & 0.16279127516355 \tabularnewline
121 & 13 & 14.4376071137797 & -1.43760711377967 \tabularnewline
122 & 16 & 16.2098123553486 & -0.209812355348638 \tabularnewline
123 & 13 & 15.986209194489 & -2.98620919448899 \tabularnewline
124 & 16 & 16.9052851925303 & -0.905285192530318 \tabularnewline
125 & 15 & 15.9764599112046 & -0.976459911204551 \tabularnewline
126 & 16 & 16.5703062660048 & -0.570306266004794 \tabularnewline
127 & 15 & 15.2861970430789 & -0.286197043078925 \tabularnewline
128 & 17 & 16.2342006335183 & 0.765799366481744 \tabularnewline
129 & 15 & 14.3031336229747 & 0.696866377025263 \tabularnewline
130 & 12 & 15.0260534181056 & -3.02605341810556 \tabularnewline
131 & 16 & 13.8622535359403 & 2.13774646405966 \tabularnewline
132 & 10 & 13.515073416576 & -3.51507341657598 \tabularnewline
133 & 16 & 13.915693286619 & 2.084306713381 \tabularnewline
134 & 12 & 14.0633474422659 & -2.06334744226588 \tabularnewline
135 & 14 & 15.6473389616941 & -1.64733896169413 \tabularnewline
136 & 15 & 15.7130883866218 & -0.713088386621779 \tabularnewline
137 & 13 & 12.9260099016928 & 0.0739900983071978 \tabularnewline
138 & 15 & 14.9448621268357 & 0.0551378731642888 \tabularnewline
139 & 11 & 13.5897538546302 & -2.58975385463017 \tabularnewline
140 & 12 & 13.4327916523459 & -1.4327916523459 \tabularnewline
141 & 8 & 13.129849093626 & -5.12984909362604 \tabularnewline
142 & 16 & 13.3231880386318 & 2.67681196136815 \tabularnewline
143 & 15 & 13.4050966853508 & 1.59490331464918 \tabularnewline
144 & 17 & 16.5181030567288 & 0.481896943271184 \tabularnewline
145 & 16 & 14.8395636072785 & 1.16043639272148 \tabularnewline
146 & 10 & 14.2104107049603 & -4.21041070496032 \tabularnewline
147 & 18 & 15.9826456387234 & 2.01735436127661 \tabularnewline
148 & 13 & 15.1112661288431 & -2.11126612884307 \tabularnewline
149 & 16 & 14.9206904929528 & 1.07930950704724 \tabularnewline
150 & 13 & 13.4256494182262 & -0.42564941822621 \tabularnewline
151 & 10 & 13.1893043096247 & -3.18930430962469 \tabularnewline
152 & 15 & 16.8206286353967 & -1.82062863539672 \tabularnewline
153 & 16 & 14.326178488058 & 1.673821511942 \tabularnewline
154 & 16 & 11.8067811052259 & 4.19321889477409 \tabularnewline
155 & 14 & 12.9449574590117 & 1.05504254098835 \tabularnewline
156 & 10 & 12.81937084268 & -2.81937084268003 \tabularnewline
157 & 17 & 17.1696452494698 & -0.169645249469839 \tabularnewline
158 & 13 & 11.7629847812285 & 1.23701521877155 \tabularnewline
159 & 15 & 14.3031336229747 & 0.696866377025263 \tabularnewline
160 & 16 & 14.9335282110495 & 1.06647178895049 \tabularnewline
161 & 12 & 12.7013603436875 & -0.701360343687461 \tabularnewline
162 & 13 & 13.0204042594097 & -0.0204042594097041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155658&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.2373949249338[/C][C]-3.23739492493382[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8715306559974[/C][C]0.128469344002595[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.1579188730686[/C][C]2.8420811269314[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.3875868582138[/C][C]3.61241314178617[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.3847124721827[/C][C]-2.38471247218274[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0588021994659[/C][C]-2.05880219946589[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.9805342535667[/C][C]3.0194657464333[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.5013046848726[/C][C]-1.50130468487265[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.6420205112279[/C][C]-1.64202051122792[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.1916323106647[/C][C]2.80836768933525[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2012604511435[/C][C]0.798739548856502[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1513754267395[/C][C]-0.151375426739527[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.2484059300545[/C][C]0.751594069945524[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5730189300958[/C][C]0.426981069904223[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5148019220329[/C][C]-0.514801922032887[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2188616944567[/C][C]-0.218861694456688[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7001421587116[/C][C]0.299857841288432[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1391742339007[/C][C]3.86082576609931[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5440308481019[/C][C]2.45596915189809[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.6257603747278[/C][C]0.374239625272177[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.0227003471923[/C][C]0.977299652807709[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7079826360392[/C][C]1.29201736396081[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5694429049519[/C][C]2.43055709504809[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.9518392431104[/C][C]1.04816075688956[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.6097901176009[/C][C]2.39020988239909[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.0230825534603[/C][C]0.97691744653966[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.2017381969037[/C][C]0.798261803096298[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.3581494183412[/C][C]-1.35814941834125[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.3581013981237[/C][C]0.641898601876343[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.8502226053004[/C][C]0.149777394699642[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.3956176207002[/C][C]-0.395617620700165[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2008535501014[/C][C]-0.200853550101422[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1086139887833[/C][C]-1.10861398878333[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.3557099781651[/C][C]0.644290021834872[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4180144778244[/C][C]-1.41801447782437[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.5420182041458[/C][C]-5.54201820414579[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.8575294906828[/C][C]-0.857529490682765[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.6788397189506[/C][C]-1.67883971895065[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.0799794679229[/C][C]1.92002053207706[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.2043913236406[/C][C]1.79560867635942[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.7017153085122[/C][C]1.2982846914878[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.8247428958967[/C][C]-1.82474289589666[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.7365218247236[/C][C]2.26347817527637[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2005970160803[/C][C]-0.200597016080268[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.8259480441368[/C][C]-0.82594804413677[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5083550362339[/C][C]-4.5083550362339[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.3138148589877[/C][C]-2.31381485898773[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.7688522223407[/C][C]0.231147777659251[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1184083629628[/C][C]0.881591637037241[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.6959181256086[/C][C]-1.69591812560865[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.2225827669305[/C][C]-0.222582766930476[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0696429423877[/C][C]-0.0696429423877041[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7424104238034[/C][C]-2.74241042380338[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.759090469678[/C][C]0.240909530321962[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]10.9706022862603[/C][C]-1.97060228626031[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.4825554214458[/C][C]1.51744457855425[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.2495961177726[/C][C]-0.249596117772642[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.7565412568989[/C][C]1.24345874310107[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.9015007011381[/C][C]0.0984992988619412[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1928871456555[/C][C]1.80711285434447[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4989818757417[/C][C]0.501018124258347[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8702336249989[/C][C]0.129766375001137[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.1702298385104[/C][C]-0.170229838510419[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.7403908930018[/C][C]-0.740390893001815[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.7629001759392[/C][C]0.237099824060773[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.4081893062432[/C][C]1.59181069375681[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3983216969772[/C][C]1.60167830302276[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.365163232186[/C][C]3.63483676781396[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8386116124382[/C][C]-3.83861161243823[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4483564405253[/C][C]0.551643559474706[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4589828170304[/C][C]-3.45898281703042[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.5443844092354[/C][C]-0.544384409235388[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.3015038935459[/C][C]1.69849610645409[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.9106036429965[/C][C]1.08939635700347[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.501548182158[/C][C]0.498451817841981[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.2053683732962[/C][C]2.7946316267038[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.6143774804502[/C][C]-0.61437748045019[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.9626375483475[/C][C]1.03736245165251[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8246909596793[/C][C]-1.82469095967932[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.9599530568513[/C][C]0.0400469431486512[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.3656410063495[/C][C]0.634358993650451[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7674003562475[/C][C]4.23259964375246[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.4697559073985[/C][C]0.53024409260146[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.7966799557444[/C][C]-0.79667995574437[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.0036075540866[/C][C]-0.0036075540866172[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.5247802621757[/C][C]1.47521973782434[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.0518560381748[/C][C]-0.051856038174789[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2089306662723[/C][C]0.791069333727733[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.8071713163743[/C][C]1.19282868362572[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2813431547033[/C][C]0.718656845296674[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.6023848014149[/C][C]-1.60238480141492[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.1696452494698[/C][C]-0.169645249469839[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3533185582066[/C][C]0.646681441793402[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2695407608268[/C][C]-0.269540760826768[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.8653899909703[/C][C]-1.86538999097025[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0710977662068[/C][C]0.928902233793175[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.956022095286[/C][C]0.0439779047139567[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]13.9601981081714[/C][C]2.03980189182863[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0038990999929[/C][C]-0.00389909999288316[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2475824871394[/C][C]-0.247582487139383[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2014054984734[/C][C]-1.20140549847342[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.930610038436[/C][C]1.06938996156396[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.5005498336332[/C][C]2.49945016636678[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.4440843588015[/C][C]0.555915641198523[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5070384488577[/C][C]1.49296155114227[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.4437644058619[/C][C]-2.44376440586185[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9901737642482[/C][C]1.00982623575185[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]16.306947515316[/C][C]-0.306947515315981[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5764413200958[/C][C]1.42355867990418[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]13.3006388375566[/C][C]-1.30063883755664[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.1031213601185[/C][C]0.896878639881471[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8863107254062[/C][C]0.113689274593845[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.2109181429146[/C][C]2.78908185708537[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.6587218559718[/C][C]-1.65872185597177[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.0250802844497[/C][C]-3.02508028444969[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.8509974794997[/C][C]1.14900252050035[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.8269486549555[/C][C]-1.82694865495546[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9077612177781[/C][C]1.09223878222193[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8522156644755[/C][C]-1.85221566447555[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.8372087248364[/C][C]0.16279127516355[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.4376071137797[/C][C]-1.43760711377967[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]16.2098123553486[/C][C]-0.209812355348638[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.986209194489[/C][C]-2.98620919448899[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.9052851925303[/C][C]-0.905285192530318[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.9764599112046[/C][C]-0.976459911204551[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.5703062660048[/C][C]-0.570306266004794[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2861970430789[/C][C]-0.286197043078925[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2342006335183[/C][C]0.765799366481744[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.3031336229747[/C][C]0.696866377025263[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0260534181056[/C][C]-3.02605341810556[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.8622535359403[/C][C]2.13774646405966[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.515073416576[/C][C]-3.51507341657598[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.915693286619[/C][C]2.084306713381[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.0633474422659[/C][C]-2.06334744226588[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.6473389616941[/C][C]-1.64733896169413[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.7130883866218[/C][C]-0.713088386621779[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.9260099016928[/C][C]0.0739900983071978[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.9448621268357[/C][C]0.0551378731642888[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.5897538546302[/C][C]-2.58975385463017[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4327916523459[/C][C]-1.4327916523459[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.129849093626[/C][C]-5.12984909362604[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.3231880386318[/C][C]2.67681196136815[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.4050966853508[/C][C]1.59490331464918[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.5181030567288[/C][C]0.481896943271184[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8395636072785[/C][C]1.16043639272148[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2104107049603[/C][C]-4.21041070496032[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.9826456387234[/C][C]2.01735436127661[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1112661288431[/C][C]-2.11126612884307[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9206904929528[/C][C]1.07930950704724[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.4256494182262[/C][C]-0.42564941822621[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.1893043096247[/C][C]-3.18930430962469[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.8206286353967[/C][C]-1.82062863539672[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.326178488058[/C][C]1.673821511942[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8067811052259[/C][C]4.19321889477409[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.9449574590117[/C][C]1.05504254098835[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.81937084268[/C][C]-2.81937084268003[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.1696452494698[/C][C]-0.169645249469839[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7629847812285[/C][C]1.23701521877155[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.3031336229747[/C][C]0.696866377025263[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.9335282110495[/C][C]1.06647178895049[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.7013603436875[/C][C]-0.701360343687461[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.0204042594097[/C][C]-0.0204042594097041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155658&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155658&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2373949249338-3.23739492493382
21615.87153065599740.128469344002595
31916.15791887306862.8420811269314
41511.38758685821383.61241314178617
51416.3847124721827-2.38471247218274
61315.0588021994659-2.05880219946589
71915.98053425356673.0194657464333
81516.5013046848726-1.50130468487265
91415.6420205112279-1.64202051122792
101512.19163231066472.80836768933525
111615.20126045114350.798739548856502
121616.1513754267395-0.151375426739527
131615.24840593005450.751594069945524
141615.57301893009580.426981069904223
151717.5148019220329-0.514801922032887
161515.2188616944567-0.218861694456688
171514.70014215871160.299857841288432
182016.13917423390073.86082576609931
191815.54403084810192.45596915189809
201615.62576037472780.374239625272177
211615.02270034719230.977299652807709
221614.70798263603921.29201736396081
231916.56944290495192.43055709504809
241614.95183924311041.04816075688956
251714.60979011760092.39020988239909
261716.02308255346030.97691744653966
271615.20173819690370.798261803096298
281516.3581494183412-1.35814941834125
291615.35810139812370.641898601876343
301413.85022260530040.149777394699642
311515.3956176207002-0.395617620700165
321212.2008535501014-0.200853550101422
331415.1086139887833-1.10861398878333
341615.35570997816510.644290021834872
351415.4180144778244-1.41801447782437
36712.5420182041458-5.54201820414579
371010.8575294906828-0.857529490682765
381415.6788397189506-1.67883971895065
391614.07997946792291.92002053207706
401614.20439132364061.79560867635942
411614.70171530851221.2982846914878
421415.8247428958967-1.82474289589666
432017.73652182472362.26347817527637
441414.2005970160803-0.200597016080268
451414.8259480441368-0.82594804413677
461115.5083550362339-4.5083550362339
471416.3138148589877-2.31381485898773
481514.76885222234070.231147777659251
491615.11840836296280.881591637037241
501415.6959181256086-1.69591812560865
511616.2225827669305-0.222582766930476
521414.0696429423877-0.0696429423877041
531214.7424104238034-2.74241042380338
541615.7590904696780.240909530321962
55910.9706022862603-1.97060228626031
561412.48255542144581.51744457855425
571616.2495961177726-0.249596117772642
581614.75654125689891.24345874310107
591514.90150070113810.0984992988619412
601614.19288714565551.80711285434447
611211.49898187574170.501018124258347
621615.87023362499890.129766375001137
631616.1702298385104-0.170229838510419
641414.7403908930018-0.740390893001815
651615.76290017593920.237099824060773
661715.40818930624321.59181069375681
671816.39832169697721.60167830302276
681814.3651632321863.63483676781396
691215.8386116124382-3.83861161243823
701615.44835644052530.551643559474706
711013.4589828170304-3.45898281703042
721414.5443844092354-0.544384409235388
731816.30150389354591.69849610645409
741816.91060364299651.08939635700347
751615.5015481821580.498451817841981
761714.20536837329622.7946316267038
771616.6143774804502-0.61437748045019
781614.96263754834751.03736245165251
791314.8246909596793-1.82469095967932
801615.95995305685130.0400469431486512
811615.36564100634950.634358993650451
822015.76740035624754.23259964375246
831615.46975590739850.53024409260146
841515.7966799557444-0.79667995574437
851515.0036075540866-0.0036075540866172
861614.52478026217571.47521973782434
871414.0518560381748-0.051856038174789
881615.20893066627230.791069333727733
891614.80717131637431.19282868362572
901514.28134315470330.718656845296674
911213.6023848014149-1.60238480141492
921717.1696452494698-0.169645249469839
931615.35331855820660.646681441793402
941515.2695407608268-0.269540760826768
951314.8653899909703-1.86538999097025
961615.07109776620680.928902233793175
971615.9560220952860.0439779047139567
981613.96019810817142.03980189182863
991616.0038990999929-0.00389909999288316
1001414.2475824871394-0.247582487139383
1011617.2014054984734-1.20140549847342
1021614.9306100384361.06938996156396
1032017.50054983363322.49945016636678
1041514.44408435880150.555915641198523
1051614.50703844885771.49296155114227
1061315.4437644058619-2.44376440586185
1071715.99017376424821.00982623575185
1081616.306947515316-0.306947515315981
1091614.57644132009581.42355867990418
1101213.3006388375566-1.30063883755664
1111615.10312136011850.896878639881471
1121615.88631072540620.113689274593845
1131714.21091814291462.78908185708537
1141314.6587218559718-1.65872185597177
1151215.0250802844497-3.02508028444969
1161816.85099747949971.14900252050035
1171415.8269486549555-1.82694865495546
1181412.90776121777811.09223878222193
1191314.8522156644755-1.85221566447555
1201615.83720872483640.16279127516355
1211314.4376071137797-1.43760711377967
1221616.2098123553486-0.209812355348638
1231315.986209194489-2.98620919448899
1241616.9052851925303-0.905285192530318
1251515.9764599112046-0.976459911204551
1261616.5703062660048-0.570306266004794
1271515.2861970430789-0.286197043078925
1281716.23420063351830.765799366481744
1291514.30313362297470.696866377025263
1301215.0260534181056-3.02605341810556
1311613.86225353594032.13774646405966
1321013.515073416576-3.51507341657598
1331613.9156932866192.084306713381
1341214.0633474422659-2.06334744226588
1351415.6473389616941-1.64733896169413
1361515.7130883866218-0.713088386621779
1371312.92600990169280.0739900983071978
1381514.94486212683570.0551378731642888
1391113.5897538546302-2.58975385463017
1401213.4327916523459-1.4327916523459
141813.129849093626-5.12984909362604
1421613.32318803863182.67681196136815
1431513.40509668535081.59490331464918
1441716.51810305672880.481896943271184
1451614.83956360727851.16043639272148
1461014.2104107049603-4.21041070496032
1471815.98264563872342.01735436127661
1481315.1112661288431-2.11126612884307
1491614.92069049295281.07930950704724
1501313.4256494182262-0.42564941822621
1511013.1893043096247-3.18930430962469
1521516.8206286353967-1.82062863539672
1531614.3261784880581.673821511942
1541611.80678110522594.19321889477409
1551412.94495745901171.05504254098835
1561012.81937084268-2.81937084268003
1571717.1696452494698-0.169645249469839
1581311.76298478122851.23701521877155
1591514.30313362297470.696866377025263
1601614.93352821104951.06647178895049
1611212.7013603436875-0.701360343687461
1621313.0204042594097-0.0204042594097041






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7549246637929470.4901506724141070.245075336207053
110.6706203550871420.6587592898257150.329379644912858
120.5541368864035790.8917262271928420.445863113596421
130.5444481821776830.9111036356446340.455551817822317
140.5320600721603620.9358798556792760.467939927839638
150.5068164677284640.9863670645430720.493183532271536
160.4403236272896790.8806472545793580.559676372710321
170.3670978845002410.7341957690004810.632902115499759
180.7430597407573920.5138805184852170.256940259242608
190.8127895653310710.3744208693378580.187210434668929
200.788797400976610.422405198046780.21120259902339
210.7366323920183820.5267352159632350.263367607981618
220.6752815104076080.6494369791847840.324718489592392
230.7233957461764040.5532085076471920.276604253823596
240.6618898358903640.6762203282192720.338110164109636
250.6291518034524640.7416963930950720.370848196547536
260.571290438899630.857419122200740.42870956110037
270.5317616593424010.9364766813151980.468238340657599
280.5163054867900770.9673890264198460.483694513209923
290.4515254238265610.9030508476531220.548474576173439
300.4505383582042420.9010767164084840.549461641795758
310.3877868065576130.7755736131152250.612213193442387
320.382427163139060.7648543262781190.61757283686094
330.345736318687640.6914726373752810.65426368131236
340.2918015202100380.5836030404200760.708198479789962
350.2936615725914650.5873231451829290.706338427408535
360.8290936306474260.3418127387051480.170906369352574
370.8089135343552970.3821729312894060.191086465644703
380.8098280586649520.3803438826700960.190171941335048
390.8252752247619120.3494495504761760.174724775238088
400.8108203659691130.3783592680617740.189179634030887
410.7841276147685360.4317447704629280.215872385231464
420.7728617246234340.4542765507531320.227138275376566
430.7781319926507460.4437360146985080.221868007349254
440.7408565789196610.5182868421606780.259143421080339
450.6993636981131460.6012726037737070.300636301886854
460.8685169614164630.2629660771670730.131483038583537
470.9019134784446480.1961730431107040.0980865215553519
480.8780783855245340.2438432289509320.121921614475466
490.8540669513850020.2918660972299960.145933048614998
500.8584002580073780.2831994839852440.141599741992622
510.8315665281909570.3368669436180850.168433471809043
520.7983314369301610.4033371261396780.201668563069839
530.834422862565530.3311542748689390.16557713743447
540.803993133609350.39201373278130.19600686639065
550.8088046374331790.3823907251336410.191195362566821
560.7946749876288210.4106500247423580.205325012371179
570.7593984669107020.4812030661785960.240601533089298
580.7371444232259090.5257111535481830.262855576774091
590.6960216245659060.6079567508681870.303978375434094
600.6949431522199340.6101136955601330.305056847780066
610.6576688447024760.6846623105950480.342331155297524
620.6130373617176270.7739252765647470.386962638282373
630.5666337113204950.866732577359010.433366288679505
640.5263507862536020.9472984274927960.473649213746398
650.4834857778184660.9669715556369320.516514222181534
660.4641385493908670.9282770987817340.535861450609133
670.450271425917740.900542851835480.54972857408226
680.599753578927710.800492842144580.40024642107229
690.7406557810855540.5186884378288920.259344218914446
700.704663235041710.5906735299165790.29533676495829
710.7861557765824720.4276884468350570.213844223417528
720.7528430637710480.4943138724579040.247156936228952
730.7446385687885320.5107228624229360.255361431211468
740.7196064972474420.5607870055051150.280393502752558
750.6819958752025040.6360082495949920.318004124797496
760.7343422397446250.531315520510750.265657760255375
770.6989616905932090.6020766188135830.301038309406791
780.670334744029490.6593305119410190.32966525597051
790.669771912628410.6604561747431810.33022808737159
800.6283861171001880.7432277657996240.371613882899812
810.5893095761308390.8213808477383220.410690423869161
820.7639152096508430.4721695806983150.236084790349157
830.7303217521050890.5393564957898220.269678247894911
840.6999880002920780.6000239994158440.300011999707922
850.6599019510887880.6801960978224230.340098048911212
860.6428659942014480.7142680115971050.357134005798552
870.5996373269531720.8007253460936570.400362673046828
880.5631676121731410.8736647756537180.436832387826859
890.5337035534107350.932592893178530.466296446589265
900.5062822919244170.9874354161511650.493717708075583
910.4937630033846880.9875260067693770.506236996615312
920.4520557400961460.9041114801922930.547944259903854
930.4126698451896770.8253396903793550.587330154810323
940.3694959919012690.7389919838025390.630504008098731
950.3680461643737470.7360923287474950.631953835626253
960.3380407507111150.6760815014222310.661959249288885
970.3011996900455010.6023993800910020.698800309954499
980.3170507143713540.6341014287427080.682949285628646
990.2771761658057020.5543523316114040.722823834194298
1000.2405156229897410.4810312459794810.75948437701026
1010.2153258131172180.4306516262344350.784674186882782
1020.2001630286881420.4003260573762840.799836971311858
1030.2347200433290110.4694400866580220.765279956670989
1040.2074683723973840.4149367447947670.792531627602616
1050.2029028060802850.405805612160570.797097193919715
1060.2075356351522860.4150712703045710.792464364847714
1070.1963107214878040.3926214429756080.803689278512196
1080.1649789837669950.329957967533990.835021016233005
1090.1689116745720280.3378233491440570.831088325427972
1100.1485339975655990.2970679951311980.851466002434401
1110.1272753539095160.2545507078190330.872724646090484
1120.1054541715084520.2109083430169040.894545828491548
1130.1608965533030550.321793106606110.839103446696945
1140.1482966402817080.2965932805634150.851703359718292
1150.1843303238321920.3686606476643830.815669676167808
1160.1637826102458670.3275652204917330.836217389754133
1170.1519548961343710.3039097922687420.848045103865629
1180.1420324702078210.2840649404156410.857967529792179
1190.1377156370280850.275431274056170.862284362971915
1200.1156564800462290.2313129600924590.884343519953771
1210.0981102252863250.196220450572650.901889774713675
1220.0837179979722070.1674359959444140.916282002027793
1230.1076046496838440.2152092993676870.892395350316156
1240.0860196202987810.1720392405975620.913980379701219
1250.06877437363602570.1375487472720510.931225626363974
1260.05272918944542520.105458378890850.947270810554575
1270.03917872349083650.0783574469816730.960821276509163
1280.03473989316443190.06947978632886380.965260106835568
1290.02561732735081310.05123465470162620.974382672649187
1300.03195933610824230.06391867221648450.968040663891758
1310.04568215687416160.09136431374832330.954317843125838
1320.06615120527512940.1323024105502590.933848794724871
1330.105428341780950.2108566835618990.89457165821905
1340.1093937150199510.2187874300399010.890606284980049
1350.08606309700816320.1721261940163260.913936902991837
1360.08325994394516630.1665198878903330.916740056054834
1370.06253683526888830.1250736705377770.937463164731112
1380.04418500563730310.08837001127460620.955814994362697
1390.1589396550857290.3178793101714580.841060344914271
1400.1285532225285220.2571064450570450.871446777471477
1410.5411381823744330.9177236352511340.458861817625567
1420.485674561667480.971349123334960.51432543833252
1430.4200502583867940.8401005167735890.579949741613206
1440.4374054261554550.8748108523109090.562594573844545
1450.4089615209101430.8179230418202860.591038479089857
1460.351591056918330.703182113836660.64840894308167
1470.4967885673126730.9935771346253450.503211432687327
1480.5700064945121890.8599870109756230.429993505487811
1490.4698033623082030.9396067246164060.530196637691797
1500.3660235920798760.7320471841597510.633976407920124
1510.3560403198416820.7120806396833640.643959680158318
1520.6664242695620480.6671514608759040.333575730437952

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.754924663792947 & 0.490150672414107 & 0.245075336207053 \tabularnewline
11 & 0.670620355087142 & 0.658759289825715 & 0.329379644912858 \tabularnewline
12 & 0.554136886403579 & 0.891726227192842 & 0.445863113596421 \tabularnewline
13 & 0.544448182177683 & 0.911103635644634 & 0.455551817822317 \tabularnewline
14 & 0.532060072160362 & 0.935879855679276 & 0.467939927839638 \tabularnewline
15 & 0.506816467728464 & 0.986367064543072 & 0.493183532271536 \tabularnewline
16 & 0.440323627289679 & 0.880647254579358 & 0.559676372710321 \tabularnewline
17 & 0.367097884500241 & 0.734195769000481 & 0.632902115499759 \tabularnewline
18 & 0.743059740757392 & 0.513880518485217 & 0.256940259242608 \tabularnewline
19 & 0.812789565331071 & 0.374420869337858 & 0.187210434668929 \tabularnewline
20 & 0.78879740097661 & 0.42240519804678 & 0.21120259902339 \tabularnewline
21 & 0.736632392018382 & 0.526735215963235 & 0.263367607981618 \tabularnewline
22 & 0.675281510407608 & 0.649436979184784 & 0.324718489592392 \tabularnewline
23 & 0.723395746176404 & 0.553208507647192 & 0.276604253823596 \tabularnewline
24 & 0.661889835890364 & 0.676220328219272 & 0.338110164109636 \tabularnewline
25 & 0.629151803452464 & 0.741696393095072 & 0.370848196547536 \tabularnewline
26 & 0.57129043889963 & 0.85741912220074 & 0.42870956110037 \tabularnewline
27 & 0.531761659342401 & 0.936476681315198 & 0.468238340657599 \tabularnewline
28 & 0.516305486790077 & 0.967389026419846 & 0.483694513209923 \tabularnewline
29 & 0.451525423826561 & 0.903050847653122 & 0.548474576173439 \tabularnewline
30 & 0.450538358204242 & 0.901076716408484 & 0.549461641795758 \tabularnewline
31 & 0.387786806557613 & 0.775573613115225 & 0.612213193442387 \tabularnewline
32 & 0.38242716313906 & 0.764854326278119 & 0.61757283686094 \tabularnewline
33 & 0.34573631868764 & 0.691472637375281 & 0.65426368131236 \tabularnewline
34 & 0.291801520210038 & 0.583603040420076 & 0.708198479789962 \tabularnewline
35 & 0.293661572591465 & 0.587323145182929 & 0.706338427408535 \tabularnewline
36 & 0.829093630647426 & 0.341812738705148 & 0.170906369352574 \tabularnewline
37 & 0.808913534355297 & 0.382172931289406 & 0.191086465644703 \tabularnewline
38 & 0.809828058664952 & 0.380343882670096 & 0.190171941335048 \tabularnewline
39 & 0.825275224761912 & 0.349449550476176 & 0.174724775238088 \tabularnewline
40 & 0.810820365969113 & 0.378359268061774 & 0.189179634030887 \tabularnewline
41 & 0.784127614768536 & 0.431744770462928 & 0.215872385231464 \tabularnewline
42 & 0.772861724623434 & 0.454276550753132 & 0.227138275376566 \tabularnewline
43 & 0.778131992650746 & 0.443736014698508 & 0.221868007349254 \tabularnewline
44 & 0.740856578919661 & 0.518286842160678 & 0.259143421080339 \tabularnewline
45 & 0.699363698113146 & 0.601272603773707 & 0.300636301886854 \tabularnewline
46 & 0.868516961416463 & 0.262966077167073 & 0.131483038583537 \tabularnewline
47 & 0.901913478444648 & 0.196173043110704 & 0.0980865215553519 \tabularnewline
48 & 0.878078385524534 & 0.243843228950932 & 0.121921614475466 \tabularnewline
49 & 0.854066951385002 & 0.291866097229996 & 0.145933048614998 \tabularnewline
50 & 0.858400258007378 & 0.283199483985244 & 0.141599741992622 \tabularnewline
51 & 0.831566528190957 & 0.336866943618085 & 0.168433471809043 \tabularnewline
52 & 0.798331436930161 & 0.403337126139678 & 0.201668563069839 \tabularnewline
53 & 0.83442286256553 & 0.331154274868939 & 0.16557713743447 \tabularnewline
54 & 0.80399313360935 & 0.3920137327813 & 0.19600686639065 \tabularnewline
55 & 0.808804637433179 & 0.382390725133641 & 0.191195362566821 \tabularnewline
56 & 0.794674987628821 & 0.410650024742358 & 0.205325012371179 \tabularnewline
57 & 0.759398466910702 & 0.481203066178596 & 0.240601533089298 \tabularnewline
58 & 0.737144423225909 & 0.525711153548183 & 0.262855576774091 \tabularnewline
59 & 0.696021624565906 & 0.607956750868187 & 0.303978375434094 \tabularnewline
60 & 0.694943152219934 & 0.610113695560133 & 0.305056847780066 \tabularnewline
61 & 0.657668844702476 & 0.684662310595048 & 0.342331155297524 \tabularnewline
62 & 0.613037361717627 & 0.773925276564747 & 0.386962638282373 \tabularnewline
63 & 0.566633711320495 & 0.86673257735901 & 0.433366288679505 \tabularnewline
64 & 0.526350786253602 & 0.947298427492796 & 0.473649213746398 \tabularnewline
65 & 0.483485777818466 & 0.966971555636932 & 0.516514222181534 \tabularnewline
66 & 0.464138549390867 & 0.928277098781734 & 0.535861450609133 \tabularnewline
67 & 0.45027142591774 & 0.90054285183548 & 0.54972857408226 \tabularnewline
68 & 0.59975357892771 & 0.80049284214458 & 0.40024642107229 \tabularnewline
69 & 0.740655781085554 & 0.518688437828892 & 0.259344218914446 \tabularnewline
70 & 0.70466323504171 & 0.590673529916579 & 0.29533676495829 \tabularnewline
71 & 0.786155776582472 & 0.427688446835057 & 0.213844223417528 \tabularnewline
72 & 0.752843063771048 & 0.494313872457904 & 0.247156936228952 \tabularnewline
73 & 0.744638568788532 & 0.510722862422936 & 0.255361431211468 \tabularnewline
74 & 0.719606497247442 & 0.560787005505115 & 0.280393502752558 \tabularnewline
75 & 0.681995875202504 & 0.636008249594992 & 0.318004124797496 \tabularnewline
76 & 0.734342239744625 & 0.53131552051075 & 0.265657760255375 \tabularnewline
77 & 0.698961690593209 & 0.602076618813583 & 0.301038309406791 \tabularnewline
78 & 0.67033474402949 & 0.659330511941019 & 0.32966525597051 \tabularnewline
79 & 0.66977191262841 & 0.660456174743181 & 0.33022808737159 \tabularnewline
80 & 0.628386117100188 & 0.743227765799624 & 0.371613882899812 \tabularnewline
81 & 0.589309576130839 & 0.821380847738322 & 0.410690423869161 \tabularnewline
82 & 0.763915209650843 & 0.472169580698315 & 0.236084790349157 \tabularnewline
83 & 0.730321752105089 & 0.539356495789822 & 0.269678247894911 \tabularnewline
84 & 0.699988000292078 & 0.600023999415844 & 0.300011999707922 \tabularnewline
85 & 0.659901951088788 & 0.680196097822423 & 0.340098048911212 \tabularnewline
86 & 0.642865994201448 & 0.714268011597105 & 0.357134005798552 \tabularnewline
87 & 0.599637326953172 & 0.800725346093657 & 0.400362673046828 \tabularnewline
88 & 0.563167612173141 & 0.873664775653718 & 0.436832387826859 \tabularnewline
89 & 0.533703553410735 & 0.93259289317853 & 0.466296446589265 \tabularnewline
90 & 0.506282291924417 & 0.987435416151165 & 0.493717708075583 \tabularnewline
91 & 0.493763003384688 & 0.987526006769377 & 0.506236996615312 \tabularnewline
92 & 0.452055740096146 & 0.904111480192293 & 0.547944259903854 \tabularnewline
93 & 0.412669845189677 & 0.825339690379355 & 0.587330154810323 \tabularnewline
94 & 0.369495991901269 & 0.738991983802539 & 0.630504008098731 \tabularnewline
95 & 0.368046164373747 & 0.736092328747495 & 0.631953835626253 \tabularnewline
96 & 0.338040750711115 & 0.676081501422231 & 0.661959249288885 \tabularnewline
97 & 0.301199690045501 & 0.602399380091002 & 0.698800309954499 \tabularnewline
98 & 0.317050714371354 & 0.634101428742708 & 0.682949285628646 \tabularnewline
99 & 0.277176165805702 & 0.554352331611404 & 0.722823834194298 \tabularnewline
100 & 0.240515622989741 & 0.481031245979481 & 0.75948437701026 \tabularnewline
101 & 0.215325813117218 & 0.430651626234435 & 0.784674186882782 \tabularnewline
102 & 0.200163028688142 & 0.400326057376284 & 0.799836971311858 \tabularnewline
103 & 0.234720043329011 & 0.469440086658022 & 0.765279956670989 \tabularnewline
104 & 0.207468372397384 & 0.414936744794767 & 0.792531627602616 \tabularnewline
105 & 0.202902806080285 & 0.40580561216057 & 0.797097193919715 \tabularnewline
106 & 0.207535635152286 & 0.415071270304571 & 0.792464364847714 \tabularnewline
107 & 0.196310721487804 & 0.392621442975608 & 0.803689278512196 \tabularnewline
108 & 0.164978983766995 & 0.32995796753399 & 0.835021016233005 \tabularnewline
109 & 0.168911674572028 & 0.337823349144057 & 0.831088325427972 \tabularnewline
110 & 0.148533997565599 & 0.297067995131198 & 0.851466002434401 \tabularnewline
111 & 0.127275353909516 & 0.254550707819033 & 0.872724646090484 \tabularnewline
112 & 0.105454171508452 & 0.210908343016904 & 0.894545828491548 \tabularnewline
113 & 0.160896553303055 & 0.32179310660611 & 0.839103446696945 \tabularnewline
114 & 0.148296640281708 & 0.296593280563415 & 0.851703359718292 \tabularnewline
115 & 0.184330323832192 & 0.368660647664383 & 0.815669676167808 \tabularnewline
116 & 0.163782610245867 & 0.327565220491733 & 0.836217389754133 \tabularnewline
117 & 0.151954896134371 & 0.303909792268742 & 0.848045103865629 \tabularnewline
118 & 0.142032470207821 & 0.284064940415641 & 0.857967529792179 \tabularnewline
119 & 0.137715637028085 & 0.27543127405617 & 0.862284362971915 \tabularnewline
120 & 0.115656480046229 & 0.231312960092459 & 0.884343519953771 \tabularnewline
121 & 0.098110225286325 & 0.19622045057265 & 0.901889774713675 \tabularnewline
122 & 0.083717997972207 & 0.167435995944414 & 0.916282002027793 \tabularnewline
123 & 0.107604649683844 & 0.215209299367687 & 0.892395350316156 \tabularnewline
124 & 0.086019620298781 & 0.172039240597562 & 0.913980379701219 \tabularnewline
125 & 0.0687743736360257 & 0.137548747272051 & 0.931225626363974 \tabularnewline
126 & 0.0527291894454252 & 0.10545837889085 & 0.947270810554575 \tabularnewline
127 & 0.0391787234908365 & 0.078357446981673 & 0.960821276509163 \tabularnewline
128 & 0.0347398931644319 & 0.0694797863288638 & 0.965260106835568 \tabularnewline
129 & 0.0256173273508131 & 0.0512346547016262 & 0.974382672649187 \tabularnewline
130 & 0.0319593361082423 & 0.0639186722164845 & 0.968040663891758 \tabularnewline
131 & 0.0456821568741616 & 0.0913643137483233 & 0.954317843125838 \tabularnewline
132 & 0.0661512052751294 & 0.132302410550259 & 0.933848794724871 \tabularnewline
133 & 0.10542834178095 & 0.210856683561899 & 0.89457165821905 \tabularnewline
134 & 0.109393715019951 & 0.218787430039901 & 0.890606284980049 \tabularnewline
135 & 0.0860630970081632 & 0.172126194016326 & 0.913936902991837 \tabularnewline
136 & 0.0832599439451663 & 0.166519887890333 & 0.916740056054834 \tabularnewline
137 & 0.0625368352688883 & 0.125073670537777 & 0.937463164731112 \tabularnewline
138 & 0.0441850056373031 & 0.0883700112746062 & 0.955814994362697 \tabularnewline
139 & 0.158939655085729 & 0.317879310171458 & 0.841060344914271 \tabularnewline
140 & 0.128553222528522 & 0.257106445057045 & 0.871446777471477 \tabularnewline
141 & 0.541138182374433 & 0.917723635251134 & 0.458861817625567 \tabularnewline
142 & 0.48567456166748 & 0.97134912333496 & 0.51432543833252 \tabularnewline
143 & 0.420050258386794 & 0.840100516773589 & 0.579949741613206 \tabularnewline
144 & 0.437405426155455 & 0.874810852310909 & 0.562594573844545 \tabularnewline
145 & 0.408961520910143 & 0.817923041820286 & 0.591038479089857 \tabularnewline
146 & 0.35159105691833 & 0.70318211383666 & 0.64840894308167 \tabularnewline
147 & 0.496788567312673 & 0.993577134625345 & 0.503211432687327 \tabularnewline
148 & 0.570006494512189 & 0.859987010975623 & 0.429993505487811 \tabularnewline
149 & 0.469803362308203 & 0.939606724616406 & 0.530196637691797 \tabularnewline
150 & 0.366023592079876 & 0.732047184159751 & 0.633976407920124 \tabularnewline
151 & 0.356040319841682 & 0.712080639683364 & 0.643959680158318 \tabularnewline
152 & 0.666424269562048 & 0.667151460875904 & 0.333575730437952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155658&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]10[/C][C]0.754924663792947[/C][C]0.490150672414107[/C][C]0.245075336207053[/C][/ROW]
[ROW][C]11[/C][C]0.670620355087142[/C][C]0.658759289825715[/C][C]0.329379644912858[/C][/ROW]
[ROW][C]12[/C][C]0.554136886403579[/C][C]0.891726227192842[/C][C]0.445863113596421[/C][/ROW]
[ROW][C]13[/C][C]0.544448182177683[/C][C]0.911103635644634[/C][C]0.455551817822317[/C][/ROW]
[ROW][C]14[/C][C]0.532060072160362[/C][C]0.935879855679276[/C][C]0.467939927839638[/C][/ROW]
[ROW][C]15[/C][C]0.506816467728464[/C][C]0.986367064543072[/C][C]0.493183532271536[/C][/ROW]
[ROW][C]16[/C][C]0.440323627289679[/C][C]0.880647254579358[/C][C]0.559676372710321[/C][/ROW]
[ROW][C]17[/C][C]0.367097884500241[/C][C]0.734195769000481[/C][C]0.632902115499759[/C][/ROW]
[ROW][C]18[/C][C]0.743059740757392[/C][C]0.513880518485217[/C][C]0.256940259242608[/C][/ROW]
[ROW][C]19[/C][C]0.812789565331071[/C][C]0.374420869337858[/C][C]0.187210434668929[/C][/ROW]
[ROW][C]20[/C][C]0.78879740097661[/C][C]0.42240519804678[/C][C]0.21120259902339[/C][/ROW]
[ROW][C]21[/C][C]0.736632392018382[/C][C]0.526735215963235[/C][C]0.263367607981618[/C][/ROW]
[ROW][C]22[/C][C]0.675281510407608[/C][C]0.649436979184784[/C][C]0.324718489592392[/C][/ROW]
[ROW][C]23[/C][C]0.723395746176404[/C][C]0.553208507647192[/C][C]0.276604253823596[/C][/ROW]
[ROW][C]24[/C][C]0.661889835890364[/C][C]0.676220328219272[/C][C]0.338110164109636[/C][/ROW]
[ROW][C]25[/C][C]0.629151803452464[/C][C]0.741696393095072[/C][C]0.370848196547536[/C][/ROW]
[ROW][C]26[/C][C]0.57129043889963[/C][C]0.85741912220074[/C][C]0.42870956110037[/C][/ROW]
[ROW][C]27[/C][C]0.531761659342401[/C][C]0.936476681315198[/C][C]0.468238340657599[/C][/ROW]
[ROW][C]28[/C][C]0.516305486790077[/C][C]0.967389026419846[/C][C]0.483694513209923[/C][/ROW]
[ROW][C]29[/C][C]0.451525423826561[/C][C]0.903050847653122[/C][C]0.548474576173439[/C][/ROW]
[ROW][C]30[/C][C]0.450538358204242[/C][C]0.901076716408484[/C][C]0.549461641795758[/C][/ROW]
[ROW][C]31[/C][C]0.387786806557613[/C][C]0.775573613115225[/C][C]0.612213193442387[/C][/ROW]
[ROW][C]32[/C][C]0.38242716313906[/C][C]0.764854326278119[/C][C]0.61757283686094[/C][/ROW]
[ROW][C]33[/C][C]0.34573631868764[/C][C]0.691472637375281[/C][C]0.65426368131236[/C][/ROW]
[ROW][C]34[/C][C]0.291801520210038[/C][C]0.583603040420076[/C][C]0.708198479789962[/C][/ROW]
[ROW][C]35[/C][C]0.293661572591465[/C][C]0.587323145182929[/C][C]0.706338427408535[/C][/ROW]
[ROW][C]36[/C][C]0.829093630647426[/C][C]0.341812738705148[/C][C]0.170906369352574[/C][/ROW]
[ROW][C]37[/C][C]0.808913534355297[/C][C]0.382172931289406[/C][C]0.191086465644703[/C][/ROW]
[ROW][C]38[/C][C]0.809828058664952[/C][C]0.380343882670096[/C][C]0.190171941335048[/C][/ROW]
[ROW][C]39[/C][C]0.825275224761912[/C][C]0.349449550476176[/C][C]0.174724775238088[/C][/ROW]
[ROW][C]40[/C][C]0.810820365969113[/C][C]0.378359268061774[/C][C]0.189179634030887[/C][/ROW]
[ROW][C]41[/C][C]0.784127614768536[/C][C]0.431744770462928[/C][C]0.215872385231464[/C][/ROW]
[ROW][C]42[/C][C]0.772861724623434[/C][C]0.454276550753132[/C][C]0.227138275376566[/C][/ROW]
[ROW][C]43[/C][C]0.778131992650746[/C][C]0.443736014698508[/C][C]0.221868007349254[/C][/ROW]
[ROW][C]44[/C][C]0.740856578919661[/C][C]0.518286842160678[/C][C]0.259143421080339[/C][/ROW]
[ROW][C]45[/C][C]0.699363698113146[/C][C]0.601272603773707[/C][C]0.300636301886854[/C][/ROW]
[ROW][C]46[/C][C]0.868516961416463[/C][C]0.262966077167073[/C][C]0.131483038583537[/C][/ROW]
[ROW][C]47[/C][C]0.901913478444648[/C][C]0.196173043110704[/C][C]0.0980865215553519[/C][/ROW]
[ROW][C]48[/C][C]0.878078385524534[/C][C]0.243843228950932[/C][C]0.121921614475466[/C][/ROW]
[ROW][C]49[/C][C]0.854066951385002[/C][C]0.291866097229996[/C][C]0.145933048614998[/C][/ROW]
[ROW][C]50[/C][C]0.858400258007378[/C][C]0.283199483985244[/C][C]0.141599741992622[/C][/ROW]
[ROW][C]51[/C][C]0.831566528190957[/C][C]0.336866943618085[/C][C]0.168433471809043[/C][/ROW]
[ROW][C]52[/C][C]0.798331436930161[/C][C]0.403337126139678[/C][C]0.201668563069839[/C][/ROW]
[ROW][C]53[/C][C]0.83442286256553[/C][C]0.331154274868939[/C][C]0.16557713743447[/C][/ROW]
[ROW][C]54[/C][C]0.80399313360935[/C][C]0.3920137327813[/C][C]0.19600686639065[/C][/ROW]
[ROW][C]55[/C][C]0.808804637433179[/C][C]0.382390725133641[/C][C]0.191195362566821[/C][/ROW]
[ROW][C]56[/C][C]0.794674987628821[/C][C]0.410650024742358[/C][C]0.205325012371179[/C][/ROW]
[ROW][C]57[/C][C]0.759398466910702[/C][C]0.481203066178596[/C][C]0.240601533089298[/C][/ROW]
[ROW][C]58[/C][C]0.737144423225909[/C][C]0.525711153548183[/C][C]0.262855576774091[/C][/ROW]
[ROW][C]59[/C][C]0.696021624565906[/C][C]0.607956750868187[/C][C]0.303978375434094[/C][/ROW]
[ROW][C]60[/C][C]0.694943152219934[/C][C]0.610113695560133[/C][C]0.305056847780066[/C][/ROW]
[ROW][C]61[/C][C]0.657668844702476[/C][C]0.684662310595048[/C][C]0.342331155297524[/C][/ROW]
[ROW][C]62[/C][C]0.613037361717627[/C][C]0.773925276564747[/C][C]0.386962638282373[/C][/ROW]
[ROW][C]63[/C][C]0.566633711320495[/C][C]0.86673257735901[/C][C]0.433366288679505[/C][/ROW]
[ROW][C]64[/C][C]0.526350786253602[/C][C]0.947298427492796[/C][C]0.473649213746398[/C][/ROW]
[ROW][C]65[/C][C]0.483485777818466[/C][C]0.966971555636932[/C][C]0.516514222181534[/C][/ROW]
[ROW][C]66[/C][C]0.464138549390867[/C][C]0.928277098781734[/C][C]0.535861450609133[/C][/ROW]
[ROW][C]67[/C][C]0.45027142591774[/C][C]0.90054285183548[/C][C]0.54972857408226[/C][/ROW]
[ROW][C]68[/C][C]0.59975357892771[/C][C]0.80049284214458[/C][C]0.40024642107229[/C][/ROW]
[ROW][C]69[/C][C]0.740655781085554[/C][C]0.518688437828892[/C][C]0.259344218914446[/C][/ROW]
[ROW][C]70[/C][C]0.70466323504171[/C][C]0.590673529916579[/C][C]0.29533676495829[/C][/ROW]
[ROW][C]71[/C][C]0.786155776582472[/C][C]0.427688446835057[/C][C]0.213844223417528[/C][/ROW]
[ROW][C]72[/C][C]0.752843063771048[/C][C]0.494313872457904[/C][C]0.247156936228952[/C][/ROW]
[ROW][C]73[/C][C]0.744638568788532[/C][C]0.510722862422936[/C][C]0.255361431211468[/C][/ROW]
[ROW][C]74[/C][C]0.719606497247442[/C][C]0.560787005505115[/C][C]0.280393502752558[/C][/ROW]
[ROW][C]75[/C][C]0.681995875202504[/C][C]0.636008249594992[/C][C]0.318004124797496[/C][/ROW]
[ROW][C]76[/C][C]0.734342239744625[/C][C]0.53131552051075[/C][C]0.265657760255375[/C][/ROW]
[ROW][C]77[/C][C]0.698961690593209[/C][C]0.602076618813583[/C][C]0.301038309406791[/C][/ROW]
[ROW][C]78[/C][C]0.67033474402949[/C][C]0.659330511941019[/C][C]0.32966525597051[/C][/ROW]
[ROW][C]79[/C][C]0.66977191262841[/C][C]0.660456174743181[/C][C]0.33022808737159[/C][/ROW]
[ROW][C]80[/C][C]0.628386117100188[/C][C]0.743227765799624[/C][C]0.371613882899812[/C][/ROW]
[ROW][C]81[/C][C]0.589309576130839[/C][C]0.821380847738322[/C][C]0.410690423869161[/C][/ROW]
[ROW][C]82[/C][C]0.763915209650843[/C][C]0.472169580698315[/C][C]0.236084790349157[/C][/ROW]
[ROW][C]83[/C][C]0.730321752105089[/C][C]0.539356495789822[/C][C]0.269678247894911[/C][/ROW]
[ROW][C]84[/C][C]0.699988000292078[/C][C]0.600023999415844[/C][C]0.300011999707922[/C][/ROW]
[ROW][C]85[/C][C]0.659901951088788[/C][C]0.680196097822423[/C][C]0.340098048911212[/C][/ROW]
[ROW][C]86[/C][C]0.642865994201448[/C][C]0.714268011597105[/C][C]0.357134005798552[/C][/ROW]
[ROW][C]87[/C][C]0.599637326953172[/C][C]0.800725346093657[/C][C]0.400362673046828[/C][/ROW]
[ROW][C]88[/C][C]0.563167612173141[/C][C]0.873664775653718[/C][C]0.436832387826859[/C][/ROW]
[ROW][C]89[/C][C]0.533703553410735[/C][C]0.93259289317853[/C][C]0.466296446589265[/C][/ROW]
[ROW][C]90[/C][C]0.506282291924417[/C][C]0.987435416151165[/C][C]0.493717708075583[/C][/ROW]
[ROW][C]91[/C][C]0.493763003384688[/C][C]0.987526006769377[/C][C]0.506236996615312[/C][/ROW]
[ROW][C]92[/C][C]0.452055740096146[/C][C]0.904111480192293[/C][C]0.547944259903854[/C][/ROW]
[ROW][C]93[/C][C]0.412669845189677[/C][C]0.825339690379355[/C][C]0.587330154810323[/C][/ROW]
[ROW][C]94[/C][C]0.369495991901269[/C][C]0.738991983802539[/C][C]0.630504008098731[/C][/ROW]
[ROW][C]95[/C][C]0.368046164373747[/C][C]0.736092328747495[/C][C]0.631953835626253[/C][/ROW]
[ROW][C]96[/C][C]0.338040750711115[/C][C]0.676081501422231[/C][C]0.661959249288885[/C][/ROW]
[ROW][C]97[/C][C]0.301199690045501[/C][C]0.602399380091002[/C][C]0.698800309954499[/C][/ROW]
[ROW][C]98[/C][C]0.317050714371354[/C][C]0.634101428742708[/C][C]0.682949285628646[/C][/ROW]
[ROW][C]99[/C][C]0.277176165805702[/C][C]0.554352331611404[/C][C]0.722823834194298[/C][/ROW]
[ROW][C]100[/C][C]0.240515622989741[/C][C]0.481031245979481[/C][C]0.75948437701026[/C][/ROW]
[ROW][C]101[/C][C]0.215325813117218[/C][C]0.430651626234435[/C][C]0.784674186882782[/C][/ROW]
[ROW][C]102[/C][C]0.200163028688142[/C][C]0.400326057376284[/C][C]0.799836971311858[/C][/ROW]
[ROW][C]103[/C][C]0.234720043329011[/C][C]0.469440086658022[/C][C]0.765279956670989[/C][/ROW]
[ROW][C]104[/C][C]0.207468372397384[/C][C]0.414936744794767[/C][C]0.792531627602616[/C][/ROW]
[ROW][C]105[/C][C]0.202902806080285[/C][C]0.40580561216057[/C][C]0.797097193919715[/C][/ROW]
[ROW][C]106[/C][C]0.207535635152286[/C][C]0.415071270304571[/C][C]0.792464364847714[/C][/ROW]
[ROW][C]107[/C][C]0.196310721487804[/C][C]0.392621442975608[/C][C]0.803689278512196[/C][/ROW]
[ROW][C]108[/C][C]0.164978983766995[/C][C]0.32995796753399[/C][C]0.835021016233005[/C][/ROW]
[ROW][C]109[/C][C]0.168911674572028[/C][C]0.337823349144057[/C][C]0.831088325427972[/C][/ROW]
[ROW][C]110[/C][C]0.148533997565599[/C][C]0.297067995131198[/C][C]0.851466002434401[/C][/ROW]
[ROW][C]111[/C][C]0.127275353909516[/C][C]0.254550707819033[/C][C]0.872724646090484[/C][/ROW]
[ROW][C]112[/C][C]0.105454171508452[/C][C]0.210908343016904[/C][C]0.894545828491548[/C][/ROW]
[ROW][C]113[/C][C]0.160896553303055[/C][C]0.32179310660611[/C][C]0.839103446696945[/C][/ROW]
[ROW][C]114[/C][C]0.148296640281708[/C][C]0.296593280563415[/C][C]0.851703359718292[/C][/ROW]
[ROW][C]115[/C][C]0.184330323832192[/C][C]0.368660647664383[/C][C]0.815669676167808[/C][/ROW]
[ROW][C]116[/C][C]0.163782610245867[/C][C]0.327565220491733[/C][C]0.836217389754133[/C][/ROW]
[ROW][C]117[/C][C]0.151954896134371[/C][C]0.303909792268742[/C][C]0.848045103865629[/C][/ROW]
[ROW][C]118[/C][C]0.142032470207821[/C][C]0.284064940415641[/C][C]0.857967529792179[/C][/ROW]
[ROW][C]119[/C][C]0.137715637028085[/C][C]0.27543127405617[/C][C]0.862284362971915[/C][/ROW]
[ROW][C]120[/C][C]0.115656480046229[/C][C]0.231312960092459[/C][C]0.884343519953771[/C][/ROW]
[ROW][C]121[/C][C]0.098110225286325[/C][C]0.19622045057265[/C][C]0.901889774713675[/C][/ROW]
[ROW][C]122[/C][C]0.083717997972207[/C][C]0.167435995944414[/C][C]0.916282002027793[/C][/ROW]
[ROW][C]123[/C][C]0.107604649683844[/C][C]0.215209299367687[/C][C]0.892395350316156[/C][/ROW]
[ROW][C]124[/C][C]0.086019620298781[/C][C]0.172039240597562[/C][C]0.913980379701219[/C][/ROW]
[ROW][C]125[/C][C]0.0687743736360257[/C][C]0.137548747272051[/C][C]0.931225626363974[/C][/ROW]
[ROW][C]126[/C][C]0.0527291894454252[/C][C]0.10545837889085[/C][C]0.947270810554575[/C][/ROW]
[ROW][C]127[/C][C]0.0391787234908365[/C][C]0.078357446981673[/C][C]0.960821276509163[/C][/ROW]
[ROW][C]128[/C][C]0.0347398931644319[/C][C]0.0694797863288638[/C][C]0.965260106835568[/C][/ROW]
[ROW][C]129[/C][C]0.0256173273508131[/C][C]0.0512346547016262[/C][C]0.974382672649187[/C][/ROW]
[ROW][C]130[/C][C]0.0319593361082423[/C][C]0.0639186722164845[/C][C]0.968040663891758[/C][/ROW]
[ROW][C]131[/C][C]0.0456821568741616[/C][C]0.0913643137483233[/C][C]0.954317843125838[/C][/ROW]
[ROW][C]132[/C][C]0.0661512052751294[/C][C]0.132302410550259[/C][C]0.933848794724871[/C][/ROW]
[ROW][C]133[/C][C]0.10542834178095[/C][C]0.210856683561899[/C][C]0.89457165821905[/C][/ROW]
[ROW][C]134[/C][C]0.109393715019951[/C][C]0.218787430039901[/C][C]0.890606284980049[/C][/ROW]
[ROW][C]135[/C][C]0.0860630970081632[/C][C]0.172126194016326[/C][C]0.913936902991837[/C][/ROW]
[ROW][C]136[/C][C]0.0832599439451663[/C][C]0.166519887890333[/C][C]0.916740056054834[/C][/ROW]
[ROW][C]137[/C][C]0.0625368352688883[/C][C]0.125073670537777[/C][C]0.937463164731112[/C][/ROW]
[ROW][C]138[/C][C]0.0441850056373031[/C][C]0.0883700112746062[/C][C]0.955814994362697[/C][/ROW]
[ROW][C]139[/C][C]0.158939655085729[/C][C]0.317879310171458[/C][C]0.841060344914271[/C][/ROW]
[ROW][C]140[/C][C]0.128553222528522[/C][C]0.257106445057045[/C][C]0.871446777471477[/C][/ROW]
[ROW][C]141[/C][C]0.541138182374433[/C][C]0.917723635251134[/C][C]0.458861817625567[/C][/ROW]
[ROW][C]142[/C][C]0.48567456166748[/C][C]0.97134912333496[/C][C]0.51432543833252[/C][/ROW]
[ROW][C]143[/C][C]0.420050258386794[/C][C]0.840100516773589[/C][C]0.579949741613206[/C][/ROW]
[ROW][C]144[/C][C]0.437405426155455[/C][C]0.874810852310909[/C][C]0.562594573844545[/C][/ROW]
[ROW][C]145[/C][C]0.408961520910143[/C][C]0.817923041820286[/C][C]0.591038479089857[/C][/ROW]
[ROW][C]146[/C][C]0.35159105691833[/C][C]0.70318211383666[/C][C]0.64840894308167[/C][/ROW]
[ROW][C]147[/C][C]0.496788567312673[/C][C]0.993577134625345[/C][C]0.503211432687327[/C][/ROW]
[ROW][C]148[/C][C]0.570006494512189[/C][C]0.859987010975623[/C][C]0.429993505487811[/C][/ROW]
[ROW][C]149[/C][C]0.469803362308203[/C][C]0.939606724616406[/C][C]0.530196637691797[/C][/ROW]
[ROW][C]150[/C][C]0.366023592079876[/C][C]0.732047184159751[/C][C]0.633976407920124[/C][/ROW]
[ROW][C]151[/C][C]0.356040319841682[/C][C]0.712080639683364[/C][C]0.643959680158318[/C][/ROW]
[ROW][C]152[/C][C]0.666424269562048[/C][C]0.667151460875904[/C][C]0.333575730437952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155658&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155658&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7549246637929470.4901506724141070.245075336207053
110.6706203550871420.6587592898257150.329379644912858
120.5541368864035790.8917262271928420.445863113596421
130.5444481821776830.9111036356446340.455551817822317
140.5320600721603620.9358798556792760.467939927839638
150.5068164677284640.9863670645430720.493183532271536
160.4403236272896790.8806472545793580.559676372710321
170.3670978845002410.7341957690004810.632902115499759
180.7430597407573920.5138805184852170.256940259242608
190.8127895653310710.3744208693378580.187210434668929
200.788797400976610.422405198046780.21120259902339
210.7366323920183820.5267352159632350.263367607981618
220.6752815104076080.6494369791847840.324718489592392
230.7233957461764040.5532085076471920.276604253823596
240.6618898358903640.6762203282192720.338110164109636
250.6291518034524640.7416963930950720.370848196547536
260.571290438899630.857419122200740.42870956110037
270.5317616593424010.9364766813151980.468238340657599
280.5163054867900770.9673890264198460.483694513209923
290.4515254238265610.9030508476531220.548474576173439
300.4505383582042420.9010767164084840.549461641795758
310.3877868065576130.7755736131152250.612213193442387
320.382427163139060.7648543262781190.61757283686094
330.345736318687640.6914726373752810.65426368131236
340.2918015202100380.5836030404200760.708198479789962
350.2936615725914650.5873231451829290.706338427408535
360.8290936306474260.3418127387051480.170906369352574
370.8089135343552970.3821729312894060.191086465644703
380.8098280586649520.3803438826700960.190171941335048
390.8252752247619120.3494495504761760.174724775238088
400.8108203659691130.3783592680617740.189179634030887
410.7841276147685360.4317447704629280.215872385231464
420.7728617246234340.4542765507531320.227138275376566
430.7781319926507460.4437360146985080.221868007349254
440.7408565789196610.5182868421606780.259143421080339
450.6993636981131460.6012726037737070.300636301886854
460.8685169614164630.2629660771670730.131483038583537
470.9019134784446480.1961730431107040.0980865215553519
480.8780783855245340.2438432289509320.121921614475466
490.8540669513850020.2918660972299960.145933048614998
500.8584002580073780.2831994839852440.141599741992622
510.8315665281909570.3368669436180850.168433471809043
520.7983314369301610.4033371261396780.201668563069839
530.834422862565530.3311542748689390.16557713743447
540.803993133609350.39201373278130.19600686639065
550.8088046374331790.3823907251336410.191195362566821
560.7946749876288210.4106500247423580.205325012371179
570.7593984669107020.4812030661785960.240601533089298
580.7371444232259090.5257111535481830.262855576774091
590.6960216245659060.6079567508681870.303978375434094
600.6949431522199340.6101136955601330.305056847780066
610.6576688447024760.6846623105950480.342331155297524
620.6130373617176270.7739252765647470.386962638282373
630.5666337113204950.866732577359010.433366288679505
640.5263507862536020.9472984274927960.473649213746398
650.4834857778184660.9669715556369320.516514222181534
660.4641385493908670.9282770987817340.535861450609133
670.450271425917740.900542851835480.54972857408226
680.599753578927710.800492842144580.40024642107229
690.7406557810855540.5186884378288920.259344218914446
700.704663235041710.5906735299165790.29533676495829
710.7861557765824720.4276884468350570.213844223417528
720.7528430637710480.4943138724579040.247156936228952
730.7446385687885320.5107228624229360.255361431211468
740.7196064972474420.5607870055051150.280393502752558
750.6819958752025040.6360082495949920.318004124797496
760.7343422397446250.531315520510750.265657760255375
770.6989616905932090.6020766188135830.301038309406791
780.670334744029490.6593305119410190.32966525597051
790.669771912628410.6604561747431810.33022808737159
800.6283861171001880.7432277657996240.371613882899812
810.5893095761308390.8213808477383220.410690423869161
820.7639152096508430.4721695806983150.236084790349157
830.7303217521050890.5393564957898220.269678247894911
840.6999880002920780.6000239994158440.300011999707922
850.6599019510887880.6801960978224230.340098048911212
860.6428659942014480.7142680115971050.357134005798552
870.5996373269531720.8007253460936570.400362673046828
880.5631676121731410.8736647756537180.436832387826859
890.5337035534107350.932592893178530.466296446589265
900.5062822919244170.9874354161511650.493717708075583
910.4937630033846880.9875260067693770.506236996615312
920.4520557400961460.9041114801922930.547944259903854
930.4126698451896770.8253396903793550.587330154810323
940.3694959919012690.7389919838025390.630504008098731
950.3680461643737470.7360923287474950.631953835626253
960.3380407507111150.6760815014222310.661959249288885
970.3011996900455010.6023993800910020.698800309954499
980.3170507143713540.6341014287427080.682949285628646
990.2771761658057020.5543523316114040.722823834194298
1000.2405156229897410.4810312459794810.75948437701026
1010.2153258131172180.4306516262344350.784674186882782
1020.2001630286881420.4003260573762840.799836971311858
1030.2347200433290110.4694400866580220.765279956670989
1040.2074683723973840.4149367447947670.792531627602616
1050.2029028060802850.405805612160570.797097193919715
1060.2075356351522860.4150712703045710.792464364847714
1070.1963107214878040.3926214429756080.803689278512196
1080.1649789837669950.329957967533990.835021016233005
1090.1689116745720280.3378233491440570.831088325427972
1100.1485339975655990.2970679951311980.851466002434401
1110.1272753539095160.2545507078190330.872724646090484
1120.1054541715084520.2109083430169040.894545828491548
1130.1608965533030550.321793106606110.839103446696945
1140.1482966402817080.2965932805634150.851703359718292
1150.1843303238321920.3686606476643830.815669676167808
1160.1637826102458670.3275652204917330.836217389754133
1170.1519548961343710.3039097922687420.848045103865629
1180.1420324702078210.2840649404156410.857967529792179
1190.1377156370280850.275431274056170.862284362971915
1200.1156564800462290.2313129600924590.884343519953771
1210.0981102252863250.196220450572650.901889774713675
1220.0837179979722070.1674359959444140.916282002027793
1230.1076046496838440.2152092993676870.892395350316156
1240.0860196202987810.1720392405975620.913980379701219
1250.06877437363602570.1375487472720510.931225626363974
1260.05272918944542520.105458378890850.947270810554575
1270.03917872349083650.0783574469816730.960821276509163
1280.03473989316443190.06947978632886380.965260106835568
1290.02561732735081310.05123465470162620.974382672649187
1300.03195933610824230.06391867221648450.968040663891758
1310.04568215687416160.09136431374832330.954317843125838
1320.06615120527512940.1323024105502590.933848794724871
1330.105428341780950.2108566835618990.89457165821905
1340.1093937150199510.2187874300399010.890606284980049
1350.08606309700816320.1721261940163260.913936902991837
1360.08325994394516630.1665198878903330.916740056054834
1370.06253683526888830.1250736705377770.937463164731112
1380.04418500563730310.08837001127460620.955814994362697
1390.1589396550857290.3178793101714580.841060344914271
1400.1285532225285220.2571064450570450.871446777471477
1410.5411381823744330.9177236352511340.458861817625567
1420.485674561667480.971349123334960.51432543833252
1430.4200502583867940.8401005167735890.579949741613206
1440.4374054261554550.8748108523109090.562594573844545
1450.4089615209101430.8179230418202860.591038479089857
1460.351591056918330.703182113836660.64840894308167
1470.4967885673126730.9935771346253450.503211432687327
1480.5700064945121890.8599870109756230.429993505487811
1490.4698033623082030.9396067246164060.530196637691797
1500.3660235920798760.7320471841597510.633976407920124
1510.3560403198416820.7120806396833640.643959680158318
1520.6664242695620480.6671514608759040.333575730437952






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.041958041958042OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.041958041958042 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155658&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.041958041958042[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155658&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155658&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.041958041958042OK


Figures (Output of Computation)

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

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