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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 13:46:03 -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/13/t1323801989ur8c8jzn5gvy551.htm/, Retrieved Thu, 02 May 2024 22:30:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154624, Retrieved Thu, 02 May 2024 22:30:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

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 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [WS 10 endogene va...] [2011-12-13 18:46:03] [cb05b01fd3da20a46af540a30bcf4c06] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	14	12
2	39	32	18	11
2	30	35	11	14
1	31	33	12	12
2	34	37	16	21
2	35	29	18	12
2	39	31	14	22
2	34	36	14	11
2	36	35	15	10
2	37	38	15	13
1	38	31	17	10
2	36	34	19	8
1	38	35	10	15
2	39	38	16	14
2	33	37	18	10
1	32	33	14	14
1	36	32	14	14
2	38	38	17	11
1	39	38	14	10
2	32	32	16	13
1	32	33	18	7
2	31	31	11	14
2	39	38	14	12
2	37	39	12	14
1	39	32	17	11
2	41	32	9	9
1	36	35	16	11
2	33	37	14	15
2	33	33	15	14
1	34	33	11	13
2	31	28	16	9
1	27	32	13	15
2	37	31	17	10
2	34	37	15	11
1	34	30	14	13
1	32	33	16	8
1	29	31	9	20
1	36	33	15	12
2	29	31	17	10
1	35	33	13	10
1	37	32	15	9
2	34	33	16	14
1	38	32	16	8
1	35	33	12	14
2	38	28	12	11
2	37	35	11	13
2	38	39	15	9
2	33	34	15	11
2	36	38	17	15
1	38	32	13	11
2	32	38	16	10
1	32	30	14	14
1	32	33	11	18
2	34	38	12	14
1	32	32	12	11
2	37	32	15	12
2	39	34	16	13
2	29	34	15	9
1	37	36	12	10
2	35	34	12	15
1	30	28	8	20
1	38	34	13	12
2	34	35	11	12
2	31	35	14	14
2	34	31	15	13
1	35	37	10	11
2	36	35	11	17
1	30	27	12	12
2	39	40	15	13
1	35	37	15	14
1	38	36	14	13
2	31	38	16	15
2	34	39	15	13
1	38	41	15	10
1	34	27	13	11
2	39	30	12	19
2	37	37	17	13
2	34	31	13	17
1	28	31	15	13
1	37	27	13	9
1	33	36	15	11
1	37	38	16	10
2	35	37	15	9
1	37	33	16	12
2	32	34	15	12
2	33	31	14	13
1	38	39	15	13
2	33	34	14	12
2	29	32	13	15
2	33	33	7	22
2	31	36	17	13
2	36	32	13	15
2	35	41	15	13
2	32	28	14	15
2	29	30	13	10
2	39	36	16	11
2	37	35	12	16
2	35	31	14	11
1	37	34	17	11
1	32	36	15	10
2	38	36	17	10
1	37	35	12	16
2	36	37	16	12
1	32	28	11	11
2	33	39	15	16
1	40	32	9	19
2	38	35	16	11
1	41	39	15	16
1	36	35	10	15
2	43	42	10	24
2	30	34	15	14
2	31	33	11	15
2	32	41	13	11
1	32	33	14	15
2	37	34	18	12
1	37	32	16	10
2	33	40	14	14
2	34	40	14	13
2	33	35	14	9
2	38	36	14	15
2	33	37	12	15
2	31	27	14	14
2	38	39	15	11
2	37	38	15	8
2	33	31	15	11
2	31	33	13	11
1	39	32	17	8
2	44	39	17	10
2	33	36	19	11
2	35	33	15	13
1	32	33	13	11
1	28	32	9	20
2	40	37	15	10
1	27	30	15	15
1	37	38	15	12
2	32	29	16	14
1	28	22	11	23
1	34	35	14	14
2	30	35	11	16
2	35	34	15	11
1	31	35	13	12
2	32	34	15	10
1	30	34	16	14
2	30	35	14	12
1	31	23	15	12
2	40	31	16	11
2	32	27	16	12
1	36	36	11	13
1	32	31	12	11
1	35	32	9	19
2	38	39	16	12
2	42	37	13	17
1	34	38	16	9
2	35	39	12	12
2	35	34	9	19
2	33	31	13	18
2	36	32	13	15
2	32	37	14	14
2	33	36	19	11
2	34	32	13	9
2	32	35	12	18
2	34	36	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154624&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154624&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154624&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 time8 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Gender[t] = -0.434592797305371 -0.00464699153391254Connected[t] + 0.0310995336660727Separate[t] + 0.054199997059966Happiness[t] + 0.0307957433946984Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Gender[t] =  -0.434592797305371 -0.00464699153391254Connected[t] +  0.0310995336660727Separate[t] +  0.054199997059966Happiness[t] +  0.0307957433946984Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154624&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Gender[t] =  -0.434592797305371 -0.00464699153391254Connected[t] +  0.0310995336660727Separate[t] +  0.054199997059966Happiness[t] +  0.0307957433946984Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154624&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154624&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
Gender[t] = -0.434592797305371 -0.00464699153391254Connected[t] + 0.0310995336660727Separate[t] + 0.054199997059966Happiness[t] + 0.0307957433946984Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4345927973053710.571755-0.76010.4483330.224166
Connected-0.004646991533912540.01174-0.39580.6927640.346382
Separate0.03109953366607270.0111582.78710.0059750.002987
Happiness0.0541999970599660.0188612.87370.0046190.00231
Depression0.03079574339469840.0138092.23020.0271550.013577

\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) & -0.434592797305371 & 0.571755 & -0.7601 & 0.448333 & 0.224166 \tabularnewline
Connected & -0.00464699153391254 & 0.01174 & -0.3958 & 0.692764 & 0.346382 \tabularnewline
Separate & 0.0310995336660727 & 0.011158 & 2.7871 & 0.005975 & 0.002987 \tabularnewline
Happiness & 0.054199997059966 & 0.018861 & 2.8737 & 0.004619 & 0.00231 \tabularnewline
Depression & 0.0307957433946984 & 0.013809 & 2.2302 & 0.027155 & 0.013577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154624&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]-0.434592797305371[/C][C]0.571755[/C][C]-0.7601[/C][C]0.448333[/C][C]0.224166[/C][/ROW]
[ROW][C]Connected[/C][C]-0.00464699153391254[/C][C]0.01174[/C][C]-0.3958[/C][C]0.692764[/C][C]0.346382[/C][/ROW]
[ROW][C]Separate[/C][C]0.0310995336660727[/C][C]0.011158[/C][C]2.7871[/C][C]0.005975[/C][C]0.002987[/C][/ROW]
[ROW][C]Happiness[/C][C]0.054199997059966[/C][C]0.018861[/C][C]2.8737[/C][C]0.004619[/C][C]0.00231[/C][/ROW]
[ROW][C]Depression[/C][C]0.0307957433946984[/C][C]0.013809[/C][C]2.2302[/C][C]0.027155[/C][C]0.013577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154624&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154624&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)-0.4345927973053710.571755-0.76010.4483330.224166
Connected-0.004646991533912540.01174-0.39580.6927640.346382
Separate0.03109953366607270.0111582.78710.0059750.002987
Happiness0.0541999970599660.0188612.87370.0046190.00231
Depression0.03079574339469840.0138092.23020.0271550.013577






Multiple Linear Regression - Regression Statistics
Multiple R0.329866888175231
R-squared0.10881216391441
Adjusted R-squared0.086106741338981
F-TEST (value)4.79234260243025
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.00112775403881449
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.464625344367291
Sum Squared Residuals33.8926435686625

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.329866888175231 \tabularnewline
R-squared & 0.10881216391441 \tabularnewline
Adjusted R-squared & 0.086106741338981 \tabularnewline
F-TEST (value) & 4.79234260243025 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.00112775403881449 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.464625344367291 \tabularnewline
Sum Squared Residuals & 33.8926435686625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154624&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.329866888175231[/C][/ROW]
[ROW][C]R-squared[/C][C]0.10881216391441[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.086106741338981[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.79234260243025[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]0.00112775403881449[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.464625344367291[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33.8926435686625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154624&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154624&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 R0.329866888175231
R-squared0.10881216391441
Adjusted R-squared0.086106741338981
F-TEST (value)4.79234260243025
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.00112775403881449
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.464625344367291
Sum Squared Residuals33.8926435686625






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.685011708690880.314988291309121
221.693712734607430.306287265392566
321.54182151017520.458178489824801
411.46758396157971-0.46758396157971
522.07200280043441-0.072002800434412
621.649797843139560.350202156860435
721.784566390043180.21543360995682
821.624545838701420.375454161298576
921.607556575632790.392443424367206
1021.788595415281190.211404584718806
1111.58226445202061-0.58226445202061
1221.731665543417190.268334456582812
1311.48124132423863-0.48124132423863
1421.864297172668030.135702827331967
1521.846296608746570.153703391253426
1611.63292845095513-0.632928450955126
1711.5832409511534-0.583240951153403
1821.830756931077820.169243068922183
1911.63271420496931-0.632714204969308
2021.679433168014290.320566831985713
2111.6341582354321-0.634158235432101
2221.4127763839770.587223616023005
2321.69430569175870.305694308241295
2421.687890701162070.312109298837933
2511.63951273754747-0.639512737547468
2621.135027291210520.864972708789481
2711.69255231608746-0.692552316087458
2821.78347533748020.216524662519798
2921.682481456481180.317518543518821
3011.4302387333127-0.430238733312705
3121.436499051305120.563500948694885
3211.60165962129335-0.601659621293348
3321.586911443554520.413088556445478
3421.709845369427460.290154630572538
3511.49954012349438-0.499540123494384
3611.55655398470687-0.556553984706867
3711.49844483329308-0.498444833293079
3811.60694899509005-0.606948995090045
3921.624087375825820.375912624174177
4011.44160450571463-0.441604505714629
4111.47881523970596-0.478815239705965
4221.732034462007230.267965537992767
4311.49757250183732-0.49757250183732
4411.51058748223346-0.510587482233456
4521.248761609117260.75123839088274
4621.478496826043110.521503173956888
4721.691864983834560.308135016165439
4821.621193759963160.378806240036843
4921.963233887724440.0367661122755647
5011.42735974084152-0.427359740841517
5121.773643139826630.226356860173372
5211.53962984995691-0.539629849956908
5311.59351143335402-0.593511433354021
5421.670732142097730.329267857902268
5511.40104169298503-0.401041692985026
5621.571202469890060.42879753010994
5721.709103294609040.290896705390956
5821.578190239309410.42180976069059
5911.47140912658506-0.471409126585056
6021.572482759294230.427517240705773
6111.34629924370098-0.346299243700982
6211.52035455156836-0.52035455156836
6321.461642057250150.538357942749848
6421.699774509821180.300225490178816
6521.584839654220420.415160345779577
6611.43419839259372-0.43419839259372
6721.606326791155820.393673208844182
6811.28563375111719-0.285633751117186
6921.841500499545510.158499500454486
7011.79758560807764-0.797585608077645
7111.66754935935517-0.66754935935517
7221.932268848334030.067731151665968
7321.8336359235490.166364076450996
7411.7848597945614-0.784859794561404
7511.2904500386468-0.290450038646804
7621.552679632073080.44732036792692
7721.865895875735050.134104124264947
7821.599622633679280.400377366320715
7911.6127216034239-0.612721603423898
8011.21491757725567-0.214917577255669
8111.6833928272953-0.683392827295302
8211.75040818215706-0.750408182157065
8321.643606891104150.356393108895847
8411.6565020006161-0.656502000616098
8521.656636494891770.343363505108232
8621.535286648694370.46471335130563
8711.81504795741335-0.815047957413354
8821.597789506297890.402210493702111
8921.592365638225520.407634361774477
9021.495247427159040.504752572840961
9121.862678291272460.137321708727544
9221.559836697488140.440163302511865
9321.891187999347240.108812000652763
9421.508226526019460.491773473980539
9521.376187853919890.623812146080114
9621.709710875151790.290289124848207
9721.625084053287170.374915946712827
9821.464401178837150.535598821162852
9911.71100578794744-0.711005787947439
10011.65724407543452-0.657244075434516
10121.737762120350970.262237879649027
10211.62508405328717-0.625084053287173
10321.78554712681430.214452873185698
10411.22244356126077-0.22244356126077
10521.930670145267010.069329854732988
10611.44763171669141-0.447631716691415
10721.683258333019630.316741666980367
10811.89349421299571-0.893494212995712
10911.49053530730646-0.490535307306456
11021.952864792783860.0471352072161382
11121.727521964748990.27247803525101
11221.505771194703840.494228805296161
11321.735137493039650.264862506960354
11411.66372419434982-0.663724194349824
11521.79600152840210.203998471597897
11611.56381098016063-0.563810980160629
11721.845978195083720.154021804916278
11821.810535460155110.189464539844889
11921.536501809779870.463498190220133
12021.729140846144570.270859153855433
12121.675075343360270.324924656639729
12221.45097824049260.549021759507398
12321.753456470623960.246543529376042
12421.63461669830770.365383301692298
12521.527895158964940.472104841035061
12621.490988215244980.509011784755023
12711.54712550736337-0.547125507363373
12821.803178772145720.196821227854284
12921.900192815535170.099807184464834
13021.642391730018660.357608269981344
13111.48634122371106-0.486341223711065
13211.53419135849306-0.534191358493064
13321.651167676829290.348832323170711
13411.64786054808113-0.647860548081135
13511.7577996718865-0.757799671886496
13621.616930310410770.383069689589233
13711.42398324613636-0.423983246136364
13811.68583353521945-0.685833535219446
13921.60341299696460.396587003035405
14021.611899776895330.388100223104668
14111.58398302597182-0.583983025971821
14221.595045008102370.404954991897629
14311.78172196180896-0.781721961808955
14421.64283001456570.357169985434301
14511.31918861609888-0.319188616098881
14621.549566215287520.450433784712483
14721.493139756289220.506860243710775
14811.5142433512431-0.514243351243098
14911.36994215931895-0.369942159318954
15011.47086667436098-0.470866674360978
15121.838452211078620.161547788921378
15221.749043903404420.25095609659558
15311.7335534133641-0.733553413364104
15421.63559319744050.364406802559504
15521.533065741693120.466934258306877
15621.63506536860790.364934631392105
15721.559836697488140.440163302511865
15821.757326585619420.242673414380583
15921.900192815535170.099807184464834
16021.384356220187770.61564377981223
16121.709910497746130.290089502253867
16221.724324558614950.27567544138505

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.68501170869088 & 0.314988291309121 \tabularnewline
2 & 2 & 1.69371273460743 & 0.306287265392566 \tabularnewline
3 & 2 & 1.5418215101752 & 0.458178489824801 \tabularnewline
4 & 1 & 1.46758396157971 & -0.46758396157971 \tabularnewline
5 & 2 & 2.07200280043441 & -0.072002800434412 \tabularnewline
6 & 2 & 1.64979784313956 & 0.350202156860435 \tabularnewline
7 & 2 & 1.78456639004318 & 0.21543360995682 \tabularnewline
8 & 2 & 1.62454583870142 & 0.375454161298576 \tabularnewline
9 & 2 & 1.60755657563279 & 0.392443424367206 \tabularnewline
10 & 2 & 1.78859541528119 & 0.211404584718806 \tabularnewline
11 & 1 & 1.58226445202061 & -0.58226445202061 \tabularnewline
12 & 2 & 1.73166554341719 & 0.268334456582812 \tabularnewline
13 & 1 & 1.48124132423863 & -0.48124132423863 \tabularnewline
14 & 2 & 1.86429717266803 & 0.135702827331967 \tabularnewline
15 & 2 & 1.84629660874657 & 0.153703391253426 \tabularnewline
16 & 1 & 1.63292845095513 & -0.632928450955126 \tabularnewline
17 & 1 & 1.5832409511534 & -0.583240951153403 \tabularnewline
18 & 2 & 1.83075693107782 & 0.169243068922183 \tabularnewline
19 & 1 & 1.63271420496931 & -0.632714204969308 \tabularnewline
20 & 2 & 1.67943316801429 & 0.320566831985713 \tabularnewline
21 & 1 & 1.6341582354321 & -0.634158235432101 \tabularnewline
22 & 2 & 1.412776383977 & 0.587223616023005 \tabularnewline
23 & 2 & 1.6943056917587 & 0.305694308241295 \tabularnewline
24 & 2 & 1.68789070116207 & 0.312109298837933 \tabularnewline
25 & 1 & 1.63951273754747 & -0.639512737547468 \tabularnewline
26 & 2 & 1.13502729121052 & 0.864972708789481 \tabularnewline
27 & 1 & 1.69255231608746 & -0.692552316087458 \tabularnewline
28 & 2 & 1.7834753374802 & 0.216524662519798 \tabularnewline
29 & 2 & 1.68248145648118 & 0.317518543518821 \tabularnewline
30 & 1 & 1.4302387333127 & -0.430238733312705 \tabularnewline
31 & 2 & 1.43649905130512 & 0.563500948694885 \tabularnewline
32 & 1 & 1.60165962129335 & -0.601659621293348 \tabularnewline
33 & 2 & 1.58691144355452 & 0.413088556445478 \tabularnewline
34 & 2 & 1.70984536942746 & 0.290154630572538 \tabularnewline
35 & 1 & 1.49954012349438 & -0.499540123494384 \tabularnewline
36 & 1 & 1.55655398470687 & -0.556553984706867 \tabularnewline
37 & 1 & 1.49844483329308 & -0.498444833293079 \tabularnewline
38 & 1 & 1.60694899509005 & -0.606948995090045 \tabularnewline
39 & 2 & 1.62408737582582 & 0.375912624174177 \tabularnewline
40 & 1 & 1.44160450571463 & -0.441604505714629 \tabularnewline
41 & 1 & 1.47881523970596 & -0.478815239705965 \tabularnewline
42 & 2 & 1.73203446200723 & 0.267965537992767 \tabularnewline
43 & 1 & 1.49757250183732 & -0.49757250183732 \tabularnewline
44 & 1 & 1.51058748223346 & -0.510587482233456 \tabularnewline
45 & 2 & 1.24876160911726 & 0.75123839088274 \tabularnewline
46 & 2 & 1.47849682604311 & 0.521503173956888 \tabularnewline
47 & 2 & 1.69186498383456 & 0.308135016165439 \tabularnewline
48 & 2 & 1.62119375996316 & 0.378806240036843 \tabularnewline
49 & 2 & 1.96323388772444 & 0.0367661122755647 \tabularnewline
50 & 1 & 1.42735974084152 & -0.427359740841517 \tabularnewline
51 & 2 & 1.77364313982663 & 0.226356860173372 \tabularnewline
52 & 1 & 1.53962984995691 & -0.539629849956908 \tabularnewline
53 & 1 & 1.59351143335402 & -0.593511433354021 \tabularnewline
54 & 2 & 1.67073214209773 & 0.329267857902268 \tabularnewline
55 & 1 & 1.40104169298503 & -0.401041692985026 \tabularnewline
56 & 2 & 1.57120246989006 & 0.42879753010994 \tabularnewline
57 & 2 & 1.70910329460904 & 0.290896705390956 \tabularnewline
58 & 2 & 1.57819023930941 & 0.42180976069059 \tabularnewline
59 & 1 & 1.47140912658506 & -0.471409126585056 \tabularnewline
60 & 2 & 1.57248275929423 & 0.427517240705773 \tabularnewline
61 & 1 & 1.34629924370098 & -0.346299243700982 \tabularnewline
62 & 1 & 1.52035455156836 & -0.52035455156836 \tabularnewline
63 & 2 & 1.46164205725015 & 0.538357942749848 \tabularnewline
64 & 2 & 1.69977450982118 & 0.300225490178816 \tabularnewline
65 & 2 & 1.58483965422042 & 0.415160345779577 \tabularnewline
66 & 1 & 1.43419839259372 & -0.43419839259372 \tabularnewline
67 & 2 & 1.60632679115582 & 0.393673208844182 \tabularnewline
68 & 1 & 1.28563375111719 & -0.285633751117186 \tabularnewline
69 & 2 & 1.84150049954551 & 0.158499500454486 \tabularnewline
70 & 1 & 1.79758560807764 & -0.797585608077645 \tabularnewline
71 & 1 & 1.66754935935517 & -0.66754935935517 \tabularnewline
72 & 2 & 1.93226884833403 & 0.067731151665968 \tabularnewline
73 & 2 & 1.833635923549 & 0.166364076450996 \tabularnewline
74 & 1 & 1.7848597945614 & -0.784859794561404 \tabularnewline
75 & 1 & 1.2904500386468 & -0.290450038646804 \tabularnewline
76 & 2 & 1.55267963207308 & 0.44732036792692 \tabularnewline
77 & 2 & 1.86589587573505 & 0.134104124264947 \tabularnewline
78 & 2 & 1.59962263367928 & 0.400377366320715 \tabularnewline
79 & 1 & 1.6127216034239 & -0.612721603423898 \tabularnewline
80 & 1 & 1.21491757725567 & -0.214917577255669 \tabularnewline
81 & 1 & 1.6833928272953 & -0.683392827295302 \tabularnewline
82 & 1 & 1.75040818215706 & -0.750408182157065 \tabularnewline
83 & 2 & 1.64360689110415 & 0.356393108895847 \tabularnewline
84 & 1 & 1.6565020006161 & -0.656502000616098 \tabularnewline
85 & 2 & 1.65663649489177 & 0.343363505108232 \tabularnewline
86 & 2 & 1.53528664869437 & 0.46471335130563 \tabularnewline
87 & 1 & 1.81504795741335 & -0.815047957413354 \tabularnewline
88 & 2 & 1.59778950629789 & 0.402210493702111 \tabularnewline
89 & 2 & 1.59236563822552 & 0.407634361774477 \tabularnewline
90 & 2 & 1.49524742715904 & 0.504752572840961 \tabularnewline
91 & 2 & 1.86267829127246 & 0.137321708727544 \tabularnewline
92 & 2 & 1.55983669748814 & 0.440163302511865 \tabularnewline
93 & 2 & 1.89118799934724 & 0.108812000652763 \tabularnewline
94 & 2 & 1.50822652601946 & 0.491773473980539 \tabularnewline
95 & 2 & 1.37618785391989 & 0.623812146080114 \tabularnewline
96 & 2 & 1.70971087515179 & 0.290289124848207 \tabularnewline
97 & 2 & 1.62508405328717 & 0.374915946712827 \tabularnewline
98 & 2 & 1.46440117883715 & 0.535598821162852 \tabularnewline
99 & 1 & 1.71100578794744 & -0.711005787947439 \tabularnewline
100 & 1 & 1.65724407543452 & -0.657244075434516 \tabularnewline
101 & 2 & 1.73776212035097 & 0.262237879649027 \tabularnewline
102 & 1 & 1.62508405328717 & -0.625084053287173 \tabularnewline
103 & 2 & 1.7855471268143 & 0.214452873185698 \tabularnewline
104 & 1 & 1.22244356126077 & -0.22244356126077 \tabularnewline
105 & 2 & 1.93067014526701 & 0.069329854732988 \tabularnewline
106 & 1 & 1.44763171669141 & -0.447631716691415 \tabularnewline
107 & 2 & 1.68325833301963 & 0.316741666980367 \tabularnewline
108 & 1 & 1.89349421299571 & -0.893494212995712 \tabularnewline
109 & 1 & 1.49053530730646 & -0.490535307306456 \tabularnewline
110 & 2 & 1.95286479278386 & 0.0471352072161382 \tabularnewline
111 & 2 & 1.72752196474899 & 0.27247803525101 \tabularnewline
112 & 2 & 1.50577119470384 & 0.494228805296161 \tabularnewline
113 & 2 & 1.73513749303965 & 0.264862506960354 \tabularnewline
114 & 1 & 1.66372419434982 & -0.663724194349824 \tabularnewline
115 & 2 & 1.7960015284021 & 0.203998471597897 \tabularnewline
116 & 1 & 1.56381098016063 & -0.563810980160629 \tabularnewline
117 & 2 & 1.84597819508372 & 0.154021804916278 \tabularnewline
118 & 2 & 1.81053546015511 & 0.189464539844889 \tabularnewline
119 & 2 & 1.53650180977987 & 0.463498190220133 \tabularnewline
120 & 2 & 1.72914084614457 & 0.270859153855433 \tabularnewline
121 & 2 & 1.67507534336027 & 0.324924656639729 \tabularnewline
122 & 2 & 1.4509782404926 & 0.549021759507398 \tabularnewline
123 & 2 & 1.75345647062396 & 0.246543529376042 \tabularnewline
124 & 2 & 1.6346166983077 & 0.365383301692298 \tabularnewline
125 & 2 & 1.52789515896494 & 0.472104841035061 \tabularnewline
126 & 2 & 1.49098821524498 & 0.509011784755023 \tabularnewline
127 & 1 & 1.54712550736337 & -0.547125507363373 \tabularnewline
128 & 2 & 1.80317877214572 & 0.196821227854284 \tabularnewline
129 & 2 & 1.90019281553517 & 0.099807184464834 \tabularnewline
130 & 2 & 1.64239173001866 & 0.357608269981344 \tabularnewline
131 & 1 & 1.48634122371106 & -0.486341223711065 \tabularnewline
132 & 1 & 1.53419135849306 & -0.534191358493064 \tabularnewline
133 & 2 & 1.65116767682929 & 0.348832323170711 \tabularnewline
134 & 1 & 1.64786054808113 & -0.647860548081135 \tabularnewline
135 & 1 & 1.7577996718865 & -0.757799671886496 \tabularnewline
136 & 2 & 1.61693031041077 & 0.383069689589233 \tabularnewline
137 & 1 & 1.42398324613636 & -0.423983246136364 \tabularnewline
138 & 1 & 1.68583353521945 & -0.685833535219446 \tabularnewline
139 & 2 & 1.6034129969646 & 0.396587003035405 \tabularnewline
140 & 2 & 1.61189977689533 & 0.388100223104668 \tabularnewline
141 & 1 & 1.58398302597182 & -0.583983025971821 \tabularnewline
142 & 2 & 1.59504500810237 & 0.404954991897629 \tabularnewline
143 & 1 & 1.78172196180896 & -0.781721961808955 \tabularnewline
144 & 2 & 1.6428300145657 & 0.357169985434301 \tabularnewline
145 & 1 & 1.31918861609888 & -0.319188616098881 \tabularnewline
146 & 2 & 1.54956621528752 & 0.450433784712483 \tabularnewline
147 & 2 & 1.49313975628922 & 0.506860243710775 \tabularnewline
148 & 1 & 1.5142433512431 & -0.514243351243098 \tabularnewline
149 & 1 & 1.36994215931895 & -0.369942159318954 \tabularnewline
150 & 1 & 1.47086667436098 & -0.470866674360978 \tabularnewline
151 & 2 & 1.83845221107862 & 0.161547788921378 \tabularnewline
152 & 2 & 1.74904390340442 & 0.25095609659558 \tabularnewline
153 & 1 & 1.7335534133641 & -0.733553413364104 \tabularnewline
154 & 2 & 1.6355931974405 & 0.364406802559504 \tabularnewline
155 & 2 & 1.53306574169312 & 0.466934258306877 \tabularnewline
156 & 2 & 1.6350653686079 & 0.364934631392105 \tabularnewline
157 & 2 & 1.55983669748814 & 0.440163302511865 \tabularnewline
158 & 2 & 1.75732658561942 & 0.242673414380583 \tabularnewline
159 & 2 & 1.90019281553517 & 0.099807184464834 \tabularnewline
160 & 2 & 1.38435622018777 & 0.61564377981223 \tabularnewline
161 & 2 & 1.70991049774613 & 0.290089502253867 \tabularnewline
162 & 2 & 1.72432455861495 & 0.27567544138505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154624&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]2[/C][C]1.68501170869088[/C][C]0.314988291309121[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]1.69371273460743[/C][C]0.306287265392566[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]1.5418215101752[/C][C]0.458178489824801[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.46758396157971[/C][C]-0.46758396157971[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]2.07200280043441[/C][C]-0.072002800434412[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.64979784313956[/C][C]0.350202156860435[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.78456639004318[/C][C]0.21543360995682[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.62454583870142[/C][C]0.375454161298576[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]1.60755657563279[/C][C]0.392443424367206[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.78859541528119[/C][C]0.211404584718806[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.58226445202061[/C][C]-0.58226445202061[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.73166554341719[/C][C]0.268334456582812[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.48124132423863[/C][C]-0.48124132423863[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.86429717266803[/C][C]0.135702827331967[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.84629660874657[/C][C]0.153703391253426[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.63292845095513[/C][C]-0.632928450955126[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.5832409511534[/C][C]-0.583240951153403[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.83075693107782[/C][C]0.169243068922183[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.63271420496931[/C][C]-0.632714204969308[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.67943316801429[/C][C]0.320566831985713[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.6341582354321[/C][C]-0.634158235432101[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.412776383977[/C][C]0.587223616023005[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.6943056917587[/C][C]0.305694308241295[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.68789070116207[/C][C]0.312109298837933[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.63951273754747[/C][C]-0.639512737547468[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]1.13502729121052[/C][C]0.864972708789481[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.69255231608746[/C][C]-0.692552316087458[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.7834753374802[/C][C]0.216524662519798[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.68248145648118[/C][C]0.317518543518821[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.4302387333127[/C][C]-0.430238733312705[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.43649905130512[/C][C]0.563500948694885[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.60165962129335[/C][C]-0.601659621293348[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]1.58691144355452[/C][C]0.413088556445478[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.70984536942746[/C][C]0.290154630572538[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.49954012349438[/C][C]-0.499540123494384[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.55655398470687[/C][C]-0.556553984706867[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.49844483329308[/C][C]-0.498444833293079[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.60694899509005[/C][C]-0.606948995090045[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.62408737582582[/C][C]0.375912624174177[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.44160450571463[/C][C]-0.441604505714629[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.47881523970596[/C][C]-0.478815239705965[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.73203446200723[/C][C]0.267965537992767[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.49757250183732[/C][C]-0.49757250183732[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.51058748223346[/C][C]-0.510587482233456[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.24876160911726[/C][C]0.75123839088274[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.47849682604311[/C][C]0.521503173956888[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.69186498383456[/C][C]0.308135016165439[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.62119375996316[/C][C]0.378806240036843[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.96323388772444[/C][C]0.0367661122755647[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]1.42735974084152[/C][C]-0.427359740841517[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.77364313982663[/C][C]0.226356860173372[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.53962984995691[/C][C]-0.539629849956908[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.59351143335402[/C][C]-0.593511433354021[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.67073214209773[/C][C]0.329267857902268[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]1.40104169298503[/C][C]-0.401041692985026[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]1.57120246989006[/C][C]0.42879753010994[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]1.70910329460904[/C][C]0.290896705390956[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]1.57819023930941[/C][C]0.42180976069059[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.47140912658506[/C][C]-0.471409126585056[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.57248275929423[/C][C]0.427517240705773[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.34629924370098[/C][C]-0.346299243700982[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.52035455156836[/C][C]-0.52035455156836[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]1.46164205725015[/C][C]0.538357942749848[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.69977450982118[/C][C]0.300225490178816[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.58483965422042[/C][C]0.415160345779577[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.43419839259372[/C][C]-0.43419839259372[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.60632679115582[/C][C]0.393673208844182[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.28563375111719[/C][C]-0.285633751117186[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]1.84150049954551[/C][C]0.158499500454486[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.79758560807764[/C][C]-0.797585608077645[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.66754935935517[/C][C]-0.66754935935517[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.93226884833403[/C][C]0.067731151665968[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.833635923549[/C][C]0.166364076450996[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.7848597945614[/C][C]-0.784859794561404[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.2904500386468[/C][C]-0.290450038646804[/C][/ROW]
[ROW][C]76[/C][C]2[/C][C]1.55267963207308[/C][C]0.44732036792692[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.86589587573505[/C][C]0.134104124264947[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.59962263367928[/C][C]0.400377366320715[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.6127216034239[/C][C]-0.612721603423898[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.21491757725567[/C][C]-0.214917577255669[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.6833928272953[/C][C]-0.683392827295302[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.75040818215706[/C][C]-0.750408182157065[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]1.64360689110415[/C][C]0.356393108895847[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.6565020006161[/C][C]-0.656502000616098[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.65663649489177[/C][C]0.343363505108232[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.53528664869437[/C][C]0.46471335130563[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.81504795741335[/C][C]-0.815047957413354[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.59778950629789[/C][C]0.402210493702111[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.59236563822552[/C][C]0.407634361774477[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]1.49524742715904[/C][C]0.504752572840961[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]1.86267829127246[/C][C]0.137321708727544[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.55983669748814[/C][C]0.440163302511865[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.89118799934724[/C][C]0.108812000652763[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]1.50822652601946[/C][C]0.491773473980539[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]1.37618785391989[/C][C]0.623812146080114[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]1.70971087515179[/C][C]0.290289124848207[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.62508405328717[/C][C]0.374915946712827[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]1.46440117883715[/C][C]0.535598821162852[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.71100578794744[/C][C]-0.711005787947439[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.65724407543452[/C][C]-0.657244075434516[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.73776212035097[/C][C]0.262237879649027[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.62508405328717[/C][C]-0.625084053287173[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.7855471268143[/C][C]0.214452873185698[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.22244356126077[/C][C]-0.22244356126077[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.93067014526701[/C][C]0.069329854732988[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.44763171669141[/C][C]-0.447631716691415[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.68325833301963[/C][C]0.316741666980367[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.89349421299571[/C][C]-0.893494212995712[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.49053530730646[/C][C]-0.490535307306456[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.95286479278386[/C][C]0.0471352072161382[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]1.72752196474899[/C][C]0.27247803525101[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]1.50577119470384[/C][C]0.494228805296161[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]1.73513749303965[/C][C]0.264862506960354[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.66372419434982[/C][C]-0.663724194349824[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]1.7960015284021[/C][C]0.203998471597897[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]1.56381098016063[/C][C]-0.563810980160629[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]1.84597819508372[/C][C]0.154021804916278[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.81053546015511[/C][C]0.189464539844889[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.53650180977987[/C][C]0.463498190220133[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]1.72914084614457[/C][C]0.270859153855433[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]1.67507534336027[/C][C]0.324924656639729[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.4509782404926[/C][C]0.549021759507398[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.75345647062396[/C][C]0.246543529376042[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]1.6346166983077[/C][C]0.365383301692298[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.52789515896494[/C][C]0.472104841035061[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.49098821524498[/C][C]0.509011784755023[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.54712550736337[/C][C]-0.547125507363373[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.80317877214572[/C][C]0.196821227854284[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.90019281553517[/C][C]0.099807184464834[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.64239173001866[/C][C]0.357608269981344[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.48634122371106[/C][C]-0.486341223711065[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.53419135849306[/C][C]-0.534191358493064[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.65116767682929[/C][C]0.348832323170711[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.64786054808113[/C][C]-0.647860548081135[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.7577996718865[/C][C]-0.757799671886496[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.61693031041077[/C][C]0.383069689589233[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.42398324613636[/C][C]-0.423983246136364[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.68583353521945[/C][C]-0.685833535219446[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.6034129969646[/C][C]0.396587003035405[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]1.61189977689533[/C][C]0.388100223104668[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.58398302597182[/C][C]-0.583983025971821[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.59504500810237[/C][C]0.404954991897629[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.78172196180896[/C][C]-0.781721961808955[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]1.6428300145657[/C][C]0.357169985434301[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.31918861609888[/C][C]-0.319188616098881[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.54956621528752[/C][C]0.450433784712483[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.49313975628922[/C][C]0.506860243710775[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]1.5142433512431[/C][C]-0.514243351243098[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.36994215931895[/C][C]-0.369942159318954[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]1.47086667436098[/C][C]-0.470866674360978[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.83845221107862[/C][C]0.161547788921378[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.74904390340442[/C][C]0.25095609659558[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.7335534133641[/C][C]-0.733553413364104[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.6355931974405[/C][C]0.364406802559504[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.53306574169312[/C][C]0.466934258306877[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]1.6350653686079[/C][C]0.364934631392105[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.55983669748814[/C][C]0.440163302511865[/C][/ROW]
[ROW][C]158[/C][C]2[/C][C]1.75732658561942[/C][C]0.242673414380583[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]1.90019281553517[/C][C]0.099807184464834[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]1.38435622018777[/C][C]0.61564377981223[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]1.70991049774613[/C][C]0.290089502253867[/C][/ROW]
[ROW][C]162[/C][C]2[/C][C]1.72432455861495[/C][C]0.27567544138505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154624&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154624&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
121.685011708690880.314988291309121
221.693712734607430.306287265392566
321.54182151017520.458178489824801
411.46758396157971-0.46758396157971
522.07200280043441-0.072002800434412
621.649797843139560.350202156860435
721.784566390043180.21543360995682
821.624545838701420.375454161298576
921.607556575632790.392443424367206
1021.788595415281190.211404584718806
1111.58226445202061-0.58226445202061
1221.731665543417190.268334456582812
1311.48124132423863-0.48124132423863
1421.864297172668030.135702827331967
1521.846296608746570.153703391253426
1611.63292845095513-0.632928450955126
1711.5832409511534-0.583240951153403
1821.830756931077820.169243068922183
1911.63271420496931-0.632714204969308
2021.679433168014290.320566831985713
2111.6341582354321-0.634158235432101
2221.4127763839770.587223616023005
2321.69430569175870.305694308241295
2421.687890701162070.312109298837933
2511.63951273754747-0.639512737547468
2621.135027291210520.864972708789481
2711.69255231608746-0.692552316087458
2821.78347533748020.216524662519798
2921.682481456481180.317518543518821
3011.4302387333127-0.430238733312705
3121.436499051305120.563500948694885
3211.60165962129335-0.601659621293348
3321.586911443554520.413088556445478
3421.709845369427460.290154630572538
3511.49954012349438-0.499540123494384
3611.55655398470687-0.556553984706867
3711.49844483329308-0.498444833293079
3811.60694899509005-0.606948995090045
3921.624087375825820.375912624174177
4011.44160450571463-0.441604505714629
4111.47881523970596-0.478815239705965
4221.732034462007230.267965537992767
4311.49757250183732-0.49757250183732
4411.51058748223346-0.510587482233456
4521.248761609117260.75123839088274
4621.478496826043110.521503173956888
4721.691864983834560.308135016165439
4821.621193759963160.378806240036843
4921.963233887724440.0367661122755647
5011.42735974084152-0.427359740841517
5121.773643139826630.226356860173372
5211.53962984995691-0.539629849956908
5311.59351143335402-0.593511433354021
5421.670732142097730.329267857902268
5511.40104169298503-0.401041692985026
5621.571202469890060.42879753010994
5721.709103294609040.290896705390956
5821.578190239309410.42180976069059
5911.47140912658506-0.471409126585056
6021.572482759294230.427517240705773
6111.34629924370098-0.346299243700982
6211.52035455156836-0.52035455156836
6321.461642057250150.538357942749848
6421.699774509821180.300225490178816
6521.584839654220420.415160345779577
6611.43419839259372-0.43419839259372
6721.606326791155820.393673208844182
6811.28563375111719-0.285633751117186
6921.841500499545510.158499500454486
7011.79758560807764-0.797585608077645
7111.66754935935517-0.66754935935517
7221.932268848334030.067731151665968
7321.8336359235490.166364076450996
7411.7848597945614-0.784859794561404
7511.2904500386468-0.290450038646804
7621.552679632073080.44732036792692
7721.865895875735050.134104124264947
7821.599622633679280.400377366320715
7911.6127216034239-0.612721603423898
8011.21491757725567-0.214917577255669
8111.6833928272953-0.683392827295302
8211.75040818215706-0.750408182157065
8321.643606891104150.356393108895847
8411.6565020006161-0.656502000616098
8521.656636494891770.343363505108232
8621.535286648694370.46471335130563
8711.81504795741335-0.815047957413354
8821.597789506297890.402210493702111
8921.592365638225520.407634361774477
9021.495247427159040.504752572840961
9121.862678291272460.137321708727544
9221.559836697488140.440163302511865
9321.891187999347240.108812000652763
9421.508226526019460.491773473980539
9521.376187853919890.623812146080114
9621.709710875151790.290289124848207
9721.625084053287170.374915946712827
9821.464401178837150.535598821162852
9911.71100578794744-0.711005787947439
10011.65724407543452-0.657244075434516
10121.737762120350970.262237879649027
10211.62508405328717-0.625084053287173
10321.78554712681430.214452873185698
10411.22244356126077-0.22244356126077
10521.930670145267010.069329854732988
10611.44763171669141-0.447631716691415
10721.683258333019630.316741666980367
10811.89349421299571-0.893494212995712
10911.49053530730646-0.490535307306456
11021.952864792783860.0471352072161382
11121.727521964748990.27247803525101
11221.505771194703840.494228805296161
11321.735137493039650.264862506960354
11411.66372419434982-0.663724194349824
11521.79600152840210.203998471597897
11611.56381098016063-0.563810980160629
11721.845978195083720.154021804916278
11821.810535460155110.189464539844889
11921.536501809779870.463498190220133
12021.729140846144570.270859153855433
12121.675075343360270.324924656639729
12221.45097824049260.549021759507398
12321.753456470623960.246543529376042
12421.63461669830770.365383301692298
12521.527895158964940.472104841035061
12621.490988215244980.509011784755023
12711.54712550736337-0.547125507363373
12821.803178772145720.196821227854284
12921.900192815535170.099807184464834
13021.642391730018660.357608269981344
13111.48634122371106-0.486341223711065
13211.53419135849306-0.534191358493064
13321.651167676829290.348832323170711
13411.64786054808113-0.647860548081135
13511.7577996718865-0.757799671886496
13621.616930310410770.383069689589233
13711.42398324613636-0.423983246136364
13811.68583353521945-0.685833535219446
13921.60341299696460.396587003035405
14021.611899776895330.388100223104668
14111.58398302597182-0.583983025971821
14221.595045008102370.404954991897629
14311.78172196180896-0.781721961808955
14421.64283001456570.357169985434301
14511.31918861609888-0.319188616098881
14621.549566215287520.450433784712483
14721.493139756289220.506860243710775
14811.5142433512431-0.514243351243098
14911.36994215931895-0.369942159318954
15011.47086667436098-0.470866674360978
15121.838452211078620.161547788921378
15221.749043903404420.25095609659558
15311.7335534133641-0.733553413364104
15421.63559319744050.364406802559504
15521.533065741693120.466934258306877
15621.63506536860790.364934631392105
15721.559836697488140.440163302511865
15821.757326585619420.242673414380583
15921.900192815535170.099807184464834
16021.384356220187770.61564377981223
16121.709910497746130.290089502253867
16221.724324558614950.27567544138505






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5334800123280320.9330399753439360.466519987671968
90.3746746820269950.7493493640539910.625325317973005
100.2412557909461960.4825115818923910.758744209053804
110.4979296564766080.9958593129532170.502070343523392
120.38647345223780.77294690447560.6135265477622
130.3884972105554560.7769944211109130.611502789444544
140.2965760227727040.5931520455454080.703423977227296
150.2291195237941030.4582390475882060.770880476205897
160.3106583234958890.6213166469917770.689341676504111
170.3234790588554060.6469581177108120.676520941144594
180.2538658295071970.5077316590143940.746134170492803
190.3467166335014820.6934332670029630.653283366498518
200.3015266575217130.6030533150434260.698473342478287
210.3928272651013680.7856545302027360.607172734898632
220.5122611031181170.9754777937637660.487738896881883
230.4701556531960890.9403113063921790.529844346803911
240.4193240989435190.8386481978870380.580675901056481
250.4468307944173860.8936615888347720.553169205582614
260.6131461219272470.7737077561455050.386853878072753
270.6758820536112260.6482358927775480.324117946388774
280.6241578532862640.7516842934274720.375842146713736
290.5910645801692540.8178708396614920.408935419830746
300.6001422266892070.7997155466215850.399857773310793
310.6371773573696120.7256452852607750.362822642630387
320.6672575459162090.6654849081675820.332742454083791
330.6493636640658850.701272671868230.350636335934115
340.6137416470858770.7725167058282470.386258352914123
350.6188623196630830.7622753606738340.381137680336917
360.6297969502216410.7404060995567180.370203049778359
370.6158024596729790.7683950806540420.384197540327021
380.6463295343632430.7073409312735140.353670465636757
390.6468810657266720.7062378685466560.353118934273328
400.6376153050313660.7247693899372670.362384694968634
410.6348922884532930.7302154230934140.365107711546707
420.6042160565170990.7915678869658020.395783943482901
430.603428243586840.793143512826320.39657175641316
440.600345991749340.799308016501320.39965400825066
450.6902792714188020.6194414571623950.309720728581198
460.6969663700996880.6060672598006240.303033629900312
470.6664821022633110.6670357954733770.333517897736689
480.6534794988537260.6930410022925470.346520501146274
490.6056634683728550.788673063254290.394336531627145
500.5979762753431980.8040474493136050.402023724656802
510.5592820583283060.8814358833433870.440717941671694
520.5602585760898960.8794828478202080.439741423910104
530.5732881708669290.8534236582661420.426711829133071
540.5461694474734940.9076611050530110.453830552526506
550.5247973215778570.9504053568442850.475202678422143
560.5201556803890120.9596886392219760.479844319610988
570.4874447546290250.974889509258050.512555245370975
580.4847889165190690.9695778330381380.515211083480931
590.4913590202824380.9827180405648750.508640979717562
600.4874986745731170.9749973491462350.512501325426883
610.4561242135406410.9122484270812820.543875786459359
620.4678149097388880.9356298194777760.532185090261112
630.4836901771466210.9673803542932410.516309822853379
640.4567536103314030.9135072206628050.543246389668597
650.4531932774584010.9063865549168010.546806722541599
660.4500070497577770.9000140995155550.549992950242223
670.4367509736428110.8735019472856210.56324902635719
680.4038286755694430.8076573511388860.596171324430557
690.3631468343900940.7262936687801890.636853165609906
700.4548218036332880.9096436072665770.545178196366712
710.5049592018197310.9900815963605380.495040798180269
720.459633783992820.919267567985640.54036621600718
730.418668960276430.837337920552860.58133103972357
740.5105554219741530.9788891560516940.489444578025847
750.4824241130254050.964848226050810.517575886974595
760.4796918678171610.9593837356343210.520308132182839
770.4380045906500420.8760091813000830.561995409349958
780.4271862981733360.8543725963466720.572813701826664
790.4537185982046080.9074371964092160.546281401795392
800.4215877999775790.8431755999551590.578412200022421
810.4724884613864250.944976922772850.527511538613575
820.547351644912820.905296710174360.45264835508718
830.5275796452054140.9448407095891720.472420354794586
840.5739879631018320.8520240737963370.426012036898168
850.5539702904233640.8920594191532720.446029709576636
860.5522603752126970.8954792495746060.447739624787303
870.64488108587520.7102378282496010.3551189141248
880.6318361171038130.7363277657923750.368163882896187
890.621103487122190.7577930257556190.37889651287781
900.6290449548444030.7419100903111940.370955045155597
910.58866917965170.82266164069660.4113308203483
920.5810005420146770.8379989159706450.418999457985323
930.5373941543221330.9252116913557340.462605845677867
940.5417262194217690.9165475611564620.458273780578231
950.5706795792552850.858640841489430.429320420744715
960.5400636028936590.9198727942126820.459936397106341
970.5231704131590120.9536591736819760.476829586840988
980.5341369048814990.9317261902370020.465863095118501
990.5998971263916410.8002057472167180.400102873608359
1000.654495970553910.6910080588921790.34550402944609
1010.6208058810108320.7583882379783360.379194118989168
1020.6552520394231120.6894959211537760.344747960576888
1030.6174377576564940.7651244846870110.382562242343506
1040.5846651706162520.8306696587674970.415334829383748
1050.5381422595783120.9237154808433760.461857740421688
1060.5344714829658430.9310570340683140.465528517034157
1070.5031520011629260.9936959976741470.496847998837073
1080.6407854450016150.718429109996770.359214554998385
1090.6603841272618020.6792317454763970.339615872738198
1100.6159915093966840.7680169812066330.384008490603316
1110.5875192021099090.8249615957801830.412480797890091
1120.590266938319250.81946612336150.40973306168075
1130.5545484816747450.8909030366505110.445451518325255
1140.6051325289463140.7897349421073730.394867471053686
1150.5604156447393450.879168710521310.439584355260655
1160.6129844044184370.7740311911631260.387015595581563
1170.5678813503349640.8642372993300720.432118649665036
1180.523324596039050.95335080792190.47667540396095
1190.5140136657381450.971972668523710.485986334261855
1200.4695982063598570.9391964127197150.530401793640143
1210.443633027743940.887266055487880.55636697225606
1220.460800404457070.921600808914140.53919959554293
1230.4144361892577420.8288723785154840.585563810742258
1240.3823735190003570.7647470380007140.617626480999643
1250.3804037660743640.7608075321487280.619596233925636
1260.4102663317376340.8205326634752670.589733668262366
1270.4834379288713080.9668758577426170.516562071128692
1280.4442612677757450.888522535551490.555738732224255
1290.3893759339200030.7787518678400050.610624066079997
1300.354244547969450.70848909593890.64575545203055
1310.3468649456059170.6937298912118340.653135054394083
1320.3242234098407840.6484468196815670.675776590159217
1330.2786515098397120.5573030196794240.721348490160288
1340.2842822469638840.5685644939277680.715717753036116
1350.4135193991361230.8270387982722460.586480600863877
1360.3866344369310480.7732688738620950.613365563068953
1370.3642976517153560.7285953034307130.635702348284644
1380.4669948533628920.9339897067257830.533005146637109
1390.4625873955550660.9251747911101320.537412604444934
1400.4192861243795610.8385722487591220.580713875620439
1410.4345632769169970.8691265538339940.565436723083003
1420.4181896713751310.8363793427502630.581810328624869
1430.6001824923262210.7996350153475570.399817507673779
1440.5696134495453680.8607731009092630.430386550454632
1450.6170142851571930.7659714296856130.382985714842807
1460.5384812641421390.9230374717157220.461518735857861
1470.4656939330429540.931387866085910.534306066957046
1480.495235088335580.990470176671160.50476491166442
1490.5151407761335050.969718447732990.484859223866495
1500.8673269399969690.2653461200060620.132673060003031
1510.8132189333448770.3735621333102460.186781066655123
1520.7117129369667490.5765741260665010.288287063033251
15317.0173464509967e-633.50867322549835e-63
15416.55334077946684e-463.27667038973342e-46

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.533480012328032 & 0.933039975343936 & 0.466519987671968 \tabularnewline
9 & 0.374674682026995 & 0.749349364053991 & 0.625325317973005 \tabularnewline
10 & 0.241255790946196 & 0.482511581892391 & 0.758744209053804 \tabularnewline
11 & 0.497929656476608 & 0.995859312953217 & 0.502070343523392 \tabularnewline
12 & 0.3864734522378 & 0.7729469044756 & 0.6135265477622 \tabularnewline
13 & 0.388497210555456 & 0.776994421110913 & 0.611502789444544 \tabularnewline
14 & 0.296576022772704 & 0.593152045545408 & 0.703423977227296 \tabularnewline
15 & 0.229119523794103 & 0.458239047588206 & 0.770880476205897 \tabularnewline
16 & 0.310658323495889 & 0.621316646991777 & 0.689341676504111 \tabularnewline
17 & 0.323479058855406 & 0.646958117710812 & 0.676520941144594 \tabularnewline
18 & 0.253865829507197 & 0.507731659014394 & 0.746134170492803 \tabularnewline
19 & 0.346716633501482 & 0.693433267002963 & 0.653283366498518 \tabularnewline
20 & 0.301526657521713 & 0.603053315043426 & 0.698473342478287 \tabularnewline
21 & 0.392827265101368 & 0.785654530202736 & 0.607172734898632 \tabularnewline
22 & 0.512261103118117 & 0.975477793763766 & 0.487738896881883 \tabularnewline
23 & 0.470155653196089 & 0.940311306392179 & 0.529844346803911 \tabularnewline
24 & 0.419324098943519 & 0.838648197887038 & 0.580675901056481 \tabularnewline
25 & 0.446830794417386 & 0.893661588834772 & 0.553169205582614 \tabularnewline
26 & 0.613146121927247 & 0.773707756145505 & 0.386853878072753 \tabularnewline
27 & 0.675882053611226 & 0.648235892777548 & 0.324117946388774 \tabularnewline
28 & 0.624157853286264 & 0.751684293427472 & 0.375842146713736 \tabularnewline
29 & 0.591064580169254 & 0.817870839661492 & 0.408935419830746 \tabularnewline
30 & 0.600142226689207 & 0.799715546621585 & 0.399857773310793 \tabularnewline
31 & 0.637177357369612 & 0.725645285260775 & 0.362822642630387 \tabularnewline
32 & 0.667257545916209 & 0.665484908167582 & 0.332742454083791 \tabularnewline
33 & 0.649363664065885 & 0.70127267186823 & 0.350636335934115 \tabularnewline
34 & 0.613741647085877 & 0.772516705828247 & 0.386258352914123 \tabularnewline
35 & 0.618862319663083 & 0.762275360673834 & 0.381137680336917 \tabularnewline
36 & 0.629796950221641 & 0.740406099556718 & 0.370203049778359 \tabularnewline
37 & 0.615802459672979 & 0.768395080654042 & 0.384197540327021 \tabularnewline
38 & 0.646329534363243 & 0.707340931273514 & 0.353670465636757 \tabularnewline
39 & 0.646881065726672 & 0.706237868546656 & 0.353118934273328 \tabularnewline
40 & 0.637615305031366 & 0.724769389937267 & 0.362384694968634 \tabularnewline
41 & 0.634892288453293 & 0.730215423093414 & 0.365107711546707 \tabularnewline
42 & 0.604216056517099 & 0.791567886965802 & 0.395783943482901 \tabularnewline
43 & 0.60342824358684 & 0.79314351282632 & 0.39657175641316 \tabularnewline
44 & 0.60034599174934 & 0.79930801650132 & 0.39965400825066 \tabularnewline
45 & 0.690279271418802 & 0.619441457162395 & 0.309720728581198 \tabularnewline
46 & 0.696966370099688 & 0.606067259800624 & 0.303033629900312 \tabularnewline
47 & 0.666482102263311 & 0.667035795473377 & 0.333517897736689 \tabularnewline
48 & 0.653479498853726 & 0.693041002292547 & 0.346520501146274 \tabularnewline
49 & 0.605663468372855 & 0.78867306325429 & 0.394336531627145 \tabularnewline
50 & 0.597976275343198 & 0.804047449313605 & 0.402023724656802 \tabularnewline
51 & 0.559282058328306 & 0.881435883343387 & 0.440717941671694 \tabularnewline
52 & 0.560258576089896 & 0.879482847820208 & 0.439741423910104 \tabularnewline
53 & 0.573288170866929 & 0.853423658266142 & 0.426711829133071 \tabularnewline
54 & 0.546169447473494 & 0.907661105053011 & 0.453830552526506 \tabularnewline
55 & 0.524797321577857 & 0.950405356844285 & 0.475202678422143 \tabularnewline
56 & 0.520155680389012 & 0.959688639221976 & 0.479844319610988 \tabularnewline
57 & 0.487444754629025 & 0.97488950925805 & 0.512555245370975 \tabularnewline
58 & 0.484788916519069 & 0.969577833038138 & 0.515211083480931 \tabularnewline
59 & 0.491359020282438 & 0.982718040564875 & 0.508640979717562 \tabularnewline
60 & 0.487498674573117 & 0.974997349146235 & 0.512501325426883 \tabularnewline
61 & 0.456124213540641 & 0.912248427081282 & 0.543875786459359 \tabularnewline
62 & 0.467814909738888 & 0.935629819477776 & 0.532185090261112 \tabularnewline
63 & 0.483690177146621 & 0.967380354293241 & 0.516309822853379 \tabularnewline
64 & 0.456753610331403 & 0.913507220662805 & 0.543246389668597 \tabularnewline
65 & 0.453193277458401 & 0.906386554916801 & 0.546806722541599 \tabularnewline
66 & 0.450007049757777 & 0.900014099515555 & 0.549992950242223 \tabularnewline
67 & 0.436750973642811 & 0.873501947285621 & 0.56324902635719 \tabularnewline
68 & 0.403828675569443 & 0.807657351138886 & 0.596171324430557 \tabularnewline
69 & 0.363146834390094 & 0.726293668780189 & 0.636853165609906 \tabularnewline
70 & 0.454821803633288 & 0.909643607266577 & 0.545178196366712 \tabularnewline
71 & 0.504959201819731 & 0.990081596360538 & 0.495040798180269 \tabularnewline
72 & 0.45963378399282 & 0.91926756798564 & 0.54036621600718 \tabularnewline
73 & 0.41866896027643 & 0.83733792055286 & 0.58133103972357 \tabularnewline
74 & 0.510555421974153 & 0.978889156051694 & 0.489444578025847 \tabularnewline
75 & 0.482424113025405 & 0.96484822605081 & 0.517575886974595 \tabularnewline
76 & 0.479691867817161 & 0.959383735634321 & 0.520308132182839 \tabularnewline
77 & 0.438004590650042 & 0.876009181300083 & 0.561995409349958 \tabularnewline
78 & 0.427186298173336 & 0.854372596346672 & 0.572813701826664 \tabularnewline
79 & 0.453718598204608 & 0.907437196409216 & 0.546281401795392 \tabularnewline
80 & 0.421587799977579 & 0.843175599955159 & 0.578412200022421 \tabularnewline
81 & 0.472488461386425 & 0.94497692277285 & 0.527511538613575 \tabularnewline
82 & 0.54735164491282 & 0.90529671017436 & 0.45264835508718 \tabularnewline
83 & 0.527579645205414 & 0.944840709589172 & 0.472420354794586 \tabularnewline
84 & 0.573987963101832 & 0.852024073796337 & 0.426012036898168 \tabularnewline
85 & 0.553970290423364 & 0.892059419153272 & 0.446029709576636 \tabularnewline
86 & 0.552260375212697 & 0.895479249574606 & 0.447739624787303 \tabularnewline
87 & 0.6448810858752 & 0.710237828249601 & 0.3551189141248 \tabularnewline
88 & 0.631836117103813 & 0.736327765792375 & 0.368163882896187 \tabularnewline
89 & 0.62110348712219 & 0.757793025755619 & 0.37889651287781 \tabularnewline
90 & 0.629044954844403 & 0.741910090311194 & 0.370955045155597 \tabularnewline
91 & 0.5886691796517 & 0.8226616406966 & 0.4113308203483 \tabularnewline
92 & 0.581000542014677 & 0.837998915970645 & 0.418999457985323 \tabularnewline
93 & 0.537394154322133 & 0.925211691355734 & 0.462605845677867 \tabularnewline
94 & 0.541726219421769 & 0.916547561156462 & 0.458273780578231 \tabularnewline
95 & 0.570679579255285 & 0.85864084148943 & 0.429320420744715 \tabularnewline
96 & 0.540063602893659 & 0.919872794212682 & 0.459936397106341 \tabularnewline
97 & 0.523170413159012 & 0.953659173681976 & 0.476829586840988 \tabularnewline
98 & 0.534136904881499 & 0.931726190237002 & 0.465863095118501 \tabularnewline
99 & 0.599897126391641 & 0.800205747216718 & 0.400102873608359 \tabularnewline
100 & 0.65449597055391 & 0.691008058892179 & 0.34550402944609 \tabularnewline
101 & 0.620805881010832 & 0.758388237978336 & 0.379194118989168 \tabularnewline
102 & 0.655252039423112 & 0.689495921153776 & 0.344747960576888 \tabularnewline
103 & 0.617437757656494 & 0.765124484687011 & 0.382562242343506 \tabularnewline
104 & 0.584665170616252 & 0.830669658767497 & 0.415334829383748 \tabularnewline
105 & 0.538142259578312 & 0.923715480843376 & 0.461857740421688 \tabularnewline
106 & 0.534471482965843 & 0.931057034068314 & 0.465528517034157 \tabularnewline
107 & 0.503152001162926 & 0.993695997674147 & 0.496847998837073 \tabularnewline
108 & 0.640785445001615 & 0.71842910999677 & 0.359214554998385 \tabularnewline
109 & 0.660384127261802 & 0.679231745476397 & 0.339615872738198 \tabularnewline
110 & 0.615991509396684 & 0.768016981206633 & 0.384008490603316 \tabularnewline
111 & 0.587519202109909 & 0.824961595780183 & 0.412480797890091 \tabularnewline
112 & 0.59026693831925 & 0.8194661233615 & 0.40973306168075 \tabularnewline
113 & 0.554548481674745 & 0.890903036650511 & 0.445451518325255 \tabularnewline
114 & 0.605132528946314 & 0.789734942107373 & 0.394867471053686 \tabularnewline
115 & 0.560415644739345 & 0.87916871052131 & 0.439584355260655 \tabularnewline
116 & 0.612984404418437 & 0.774031191163126 & 0.387015595581563 \tabularnewline
117 & 0.567881350334964 & 0.864237299330072 & 0.432118649665036 \tabularnewline
118 & 0.52332459603905 & 0.9533508079219 & 0.47667540396095 \tabularnewline
119 & 0.514013665738145 & 0.97197266852371 & 0.485986334261855 \tabularnewline
120 & 0.469598206359857 & 0.939196412719715 & 0.530401793640143 \tabularnewline
121 & 0.44363302774394 & 0.88726605548788 & 0.55636697225606 \tabularnewline
122 & 0.46080040445707 & 0.92160080891414 & 0.53919959554293 \tabularnewline
123 & 0.414436189257742 & 0.828872378515484 & 0.585563810742258 \tabularnewline
124 & 0.382373519000357 & 0.764747038000714 & 0.617626480999643 \tabularnewline
125 & 0.380403766074364 & 0.760807532148728 & 0.619596233925636 \tabularnewline
126 & 0.410266331737634 & 0.820532663475267 & 0.589733668262366 \tabularnewline
127 & 0.483437928871308 & 0.966875857742617 & 0.516562071128692 \tabularnewline
128 & 0.444261267775745 & 0.88852253555149 & 0.555738732224255 \tabularnewline
129 & 0.389375933920003 & 0.778751867840005 & 0.610624066079997 \tabularnewline
130 & 0.35424454796945 & 0.7084890959389 & 0.64575545203055 \tabularnewline
131 & 0.346864945605917 & 0.693729891211834 & 0.653135054394083 \tabularnewline
132 & 0.324223409840784 & 0.648446819681567 & 0.675776590159217 \tabularnewline
133 & 0.278651509839712 & 0.557303019679424 & 0.721348490160288 \tabularnewline
134 & 0.284282246963884 & 0.568564493927768 & 0.715717753036116 \tabularnewline
135 & 0.413519399136123 & 0.827038798272246 & 0.586480600863877 \tabularnewline
136 & 0.386634436931048 & 0.773268873862095 & 0.613365563068953 \tabularnewline
137 & 0.364297651715356 & 0.728595303430713 & 0.635702348284644 \tabularnewline
138 & 0.466994853362892 & 0.933989706725783 & 0.533005146637109 \tabularnewline
139 & 0.462587395555066 & 0.925174791110132 & 0.537412604444934 \tabularnewline
140 & 0.419286124379561 & 0.838572248759122 & 0.580713875620439 \tabularnewline
141 & 0.434563276916997 & 0.869126553833994 & 0.565436723083003 \tabularnewline
142 & 0.418189671375131 & 0.836379342750263 & 0.581810328624869 \tabularnewline
143 & 0.600182492326221 & 0.799635015347557 & 0.399817507673779 \tabularnewline
144 & 0.569613449545368 & 0.860773100909263 & 0.430386550454632 \tabularnewline
145 & 0.617014285157193 & 0.765971429685613 & 0.382985714842807 \tabularnewline
146 & 0.538481264142139 & 0.923037471715722 & 0.461518735857861 \tabularnewline
147 & 0.465693933042954 & 0.93138786608591 & 0.534306066957046 \tabularnewline
148 & 0.49523508833558 & 0.99047017667116 & 0.50476491166442 \tabularnewline
149 & 0.515140776133505 & 0.96971844773299 & 0.484859223866495 \tabularnewline
150 & 0.867326939996969 & 0.265346120006062 & 0.132673060003031 \tabularnewline
151 & 0.813218933344877 & 0.373562133310246 & 0.186781066655123 \tabularnewline
152 & 0.711712936966749 & 0.576574126066501 & 0.288287063033251 \tabularnewline
153 & 1 & 7.0173464509967e-63 & 3.50867322549835e-63 \tabularnewline
154 & 1 & 6.55334077946684e-46 & 3.27667038973342e-46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154624&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.533480012328032[/C][C]0.933039975343936[/C][C]0.466519987671968[/C][/ROW]
[ROW][C]9[/C][C]0.374674682026995[/C][C]0.749349364053991[/C][C]0.625325317973005[/C][/ROW]
[ROW][C]10[/C][C]0.241255790946196[/C][C]0.482511581892391[/C][C]0.758744209053804[/C][/ROW]
[ROW][C]11[/C][C]0.497929656476608[/C][C]0.995859312953217[/C][C]0.502070343523392[/C][/ROW]
[ROW][C]12[/C][C]0.3864734522378[/C][C]0.7729469044756[/C][C]0.6135265477622[/C][/ROW]
[ROW][C]13[/C][C]0.388497210555456[/C][C]0.776994421110913[/C][C]0.611502789444544[/C][/ROW]
[ROW][C]14[/C][C]0.296576022772704[/C][C]0.593152045545408[/C][C]0.703423977227296[/C][/ROW]
[ROW][C]15[/C][C]0.229119523794103[/C][C]0.458239047588206[/C][C]0.770880476205897[/C][/ROW]
[ROW][C]16[/C][C]0.310658323495889[/C][C]0.621316646991777[/C][C]0.689341676504111[/C][/ROW]
[ROW][C]17[/C][C]0.323479058855406[/C][C]0.646958117710812[/C][C]0.676520941144594[/C][/ROW]
[ROW][C]18[/C][C]0.253865829507197[/C][C]0.507731659014394[/C][C]0.746134170492803[/C][/ROW]
[ROW][C]19[/C][C]0.346716633501482[/C][C]0.693433267002963[/C][C]0.653283366498518[/C][/ROW]
[ROW][C]20[/C][C]0.301526657521713[/C][C]0.603053315043426[/C][C]0.698473342478287[/C][/ROW]
[ROW][C]21[/C][C]0.392827265101368[/C][C]0.785654530202736[/C][C]0.607172734898632[/C][/ROW]
[ROW][C]22[/C][C]0.512261103118117[/C][C]0.975477793763766[/C][C]0.487738896881883[/C][/ROW]
[ROW][C]23[/C][C]0.470155653196089[/C][C]0.940311306392179[/C][C]0.529844346803911[/C][/ROW]
[ROW][C]24[/C][C]0.419324098943519[/C][C]0.838648197887038[/C][C]0.580675901056481[/C][/ROW]
[ROW][C]25[/C][C]0.446830794417386[/C][C]0.893661588834772[/C][C]0.553169205582614[/C][/ROW]
[ROW][C]26[/C][C]0.613146121927247[/C][C]0.773707756145505[/C][C]0.386853878072753[/C][/ROW]
[ROW][C]27[/C][C]0.675882053611226[/C][C]0.648235892777548[/C][C]0.324117946388774[/C][/ROW]
[ROW][C]28[/C][C]0.624157853286264[/C][C]0.751684293427472[/C][C]0.375842146713736[/C][/ROW]
[ROW][C]29[/C][C]0.591064580169254[/C][C]0.817870839661492[/C][C]0.408935419830746[/C][/ROW]
[ROW][C]30[/C][C]0.600142226689207[/C][C]0.799715546621585[/C][C]0.399857773310793[/C][/ROW]
[ROW][C]31[/C][C]0.637177357369612[/C][C]0.725645285260775[/C][C]0.362822642630387[/C][/ROW]
[ROW][C]32[/C][C]0.667257545916209[/C][C]0.665484908167582[/C][C]0.332742454083791[/C][/ROW]
[ROW][C]33[/C][C]0.649363664065885[/C][C]0.70127267186823[/C][C]0.350636335934115[/C][/ROW]
[ROW][C]34[/C][C]0.613741647085877[/C][C]0.772516705828247[/C][C]0.386258352914123[/C][/ROW]
[ROW][C]35[/C][C]0.618862319663083[/C][C]0.762275360673834[/C][C]0.381137680336917[/C][/ROW]
[ROW][C]36[/C][C]0.629796950221641[/C][C]0.740406099556718[/C][C]0.370203049778359[/C][/ROW]
[ROW][C]37[/C][C]0.615802459672979[/C][C]0.768395080654042[/C][C]0.384197540327021[/C][/ROW]
[ROW][C]38[/C][C]0.646329534363243[/C][C]0.707340931273514[/C][C]0.353670465636757[/C][/ROW]
[ROW][C]39[/C][C]0.646881065726672[/C][C]0.706237868546656[/C][C]0.353118934273328[/C][/ROW]
[ROW][C]40[/C][C]0.637615305031366[/C][C]0.724769389937267[/C][C]0.362384694968634[/C][/ROW]
[ROW][C]41[/C][C]0.634892288453293[/C][C]0.730215423093414[/C][C]0.365107711546707[/C][/ROW]
[ROW][C]42[/C][C]0.604216056517099[/C][C]0.791567886965802[/C][C]0.395783943482901[/C][/ROW]
[ROW][C]43[/C][C]0.60342824358684[/C][C]0.79314351282632[/C][C]0.39657175641316[/C][/ROW]
[ROW][C]44[/C][C]0.60034599174934[/C][C]0.79930801650132[/C][C]0.39965400825066[/C][/ROW]
[ROW][C]45[/C][C]0.690279271418802[/C][C]0.619441457162395[/C][C]0.309720728581198[/C][/ROW]
[ROW][C]46[/C][C]0.696966370099688[/C][C]0.606067259800624[/C][C]0.303033629900312[/C][/ROW]
[ROW][C]47[/C][C]0.666482102263311[/C][C]0.667035795473377[/C][C]0.333517897736689[/C][/ROW]
[ROW][C]48[/C][C]0.653479498853726[/C][C]0.693041002292547[/C][C]0.346520501146274[/C][/ROW]
[ROW][C]49[/C][C]0.605663468372855[/C][C]0.78867306325429[/C][C]0.394336531627145[/C][/ROW]
[ROW][C]50[/C][C]0.597976275343198[/C][C]0.804047449313605[/C][C]0.402023724656802[/C][/ROW]
[ROW][C]51[/C][C]0.559282058328306[/C][C]0.881435883343387[/C][C]0.440717941671694[/C][/ROW]
[ROW][C]52[/C][C]0.560258576089896[/C][C]0.879482847820208[/C][C]0.439741423910104[/C][/ROW]
[ROW][C]53[/C][C]0.573288170866929[/C][C]0.853423658266142[/C][C]0.426711829133071[/C][/ROW]
[ROW][C]54[/C][C]0.546169447473494[/C][C]0.907661105053011[/C][C]0.453830552526506[/C][/ROW]
[ROW][C]55[/C][C]0.524797321577857[/C][C]0.950405356844285[/C][C]0.475202678422143[/C][/ROW]
[ROW][C]56[/C][C]0.520155680389012[/C][C]0.959688639221976[/C][C]0.479844319610988[/C][/ROW]
[ROW][C]57[/C][C]0.487444754629025[/C][C]0.97488950925805[/C][C]0.512555245370975[/C][/ROW]
[ROW][C]58[/C][C]0.484788916519069[/C][C]0.969577833038138[/C][C]0.515211083480931[/C][/ROW]
[ROW][C]59[/C][C]0.491359020282438[/C][C]0.982718040564875[/C][C]0.508640979717562[/C][/ROW]
[ROW][C]60[/C][C]0.487498674573117[/C][C]0.974997349146235[/C][C]0.512501325426883[/C][/ROW]
[ROW][C]61[/C][C]0.456124213540641[/C][C]0.912248427081282[/C][C]0.543875786459359[/C][/ROW]
[ROW][C]62[/C][C]0.467814909738888[/C][C]0.935629819477776[/C][C]0.532185090261112[/C][/ROW]
[ROW][C]63[/C][C]0.483690177146621[/C][C]0.967380354293241[/C][C]0.516309822853379[/C][/ROW]
[ROW][C]64[/C][C]0.456753610331403[/C][C]0.913507220662805[/C][C]0.543246389668597[/C][/ROW]
[ROW][C]65[/C][C]0.453193277458401[/C][C]0.906386554916801[/C][C]0.546806722541599[/C][/ROW]
[ROW][C]66[/C][C]0.450007049757777[/C][C]0.900014099515555[/C][C]0.549992950242223[/C][/ROW]
[ROW][C]67[/C][C]0.436750973642811[/C][C]0.873501947285621[/C][C]0.56324902635719[/C][/ROW]
[ROW][C]68[/C][C]0.403828675569443[/C][C]0.807657351138886[/C][C]0.596171324430557[/C][/ROW]
[ROW][C]69[/C][C]0.363146834390094[/C][C]0.726293668780189[/C][C]0.636853165609906[/C][/ROW]
[ROW][C]70[/C][C]0.454821803633288[/C][C]0.909643607266577[/C][C]0.545178196366712[/C][/ROW]
[ROW][C]71[/C][C]0.504959201819731[/C][C]0.990081596360538[/C][C]0.495040798180269[/C][/ROW]
[ROW][C]72[/C][C]0.45963378399282[/C][C]0.91926756798564[/C][C]0.54036621600718[/C][/ROW]
[ROW][C]73[/C][C]0.41866896027643[/C][C]0.83733792055286[/C][C]0.58133103972357[/C][/ROW]
[ROW][C]74[/C][C]0.510555421974153[/C][C]0.978889156051694[/C][C]0.489444578025847[/C][/ROW]
[ROW][C]75[/C][C]0.482424113025405[/C][C]0.96484822605081[/C][C]0.517575886974595[/C][/ROW]
[ROW][C]76[/C][C]0.479691867817161[/C][C]0.959383735634321[/C][C]0.520308132182839[/C][/ROW]
[ROW][C]77[/C][C]0.438004590650042[/C][C]0.876009181300083[/C][C]0.561995409349958[/C][/ROW]
[ROW][C]78[/C][C]0.427186298173336[/C][C]0.854372596346672[/C][C]0.572813701826664[/C][/ROW]
[ROW][C]79[/C][C]0.453718598204608[/C][C]0.907437196409216[/C][C]0.546281401795392[/C][/ROW]
[ROW][C]80[/C][C]0.421587799977579[/C][C]0.843175599955159[/C][C]0.578412200022421[/C][/ROW]
[ROW][C]81[/C][C]0.472488461386425[/C][C]0.94497692277285[/C][C]0.527511538613575[/C][/ROW]
[ROW][C]82[/C][C]0.54735164491282[/C][C]0.90529671017436[/C][C]0.45264835508718[/C][/ROW]
[ROW][C]83[/C][C]0.527579645205414[/C][C]0.944840709589172[/C][C]0.472420354794586[/C][/ROW]
[ROW][C]84[/C][C]0.573987963101832[/C][C]0.852024073796337[/C][C]0.426012036898168[/C][/ROW]
[ROW][C]85[/C][C]0.553970290423364[/C][C]0.892059419153272[/C][C]0.446029709576636[/C][/ROW]
[ROW][C]86[/C][C]0.552260375212697[/C][C]0.895479249574606[/C][C]0.447739624787303[/C][/ROW]
[ROW][C]87[/C][C]0.6448810858752[/C][C]0.710237828249601[/C][C]0.3551189141248[/C][/ROW]
[ROW][C]88[/C][C]0.631836117103813[/C][C]0.736327765792375[/C][C]0.368163882896187[/C][/ROW]
[ROW][C]89[/C][C]0.62110348712219[/C][C]0.757793025755619[/C][C]0.37889651287781[/C][/ROW]
[ROW][C]90[/C][C]0.629044954844403[/C][C]0.741910090311194[/C][C]0.370955045155597[/C][/ROW]
[ROW][C]91[/C][C]0.5886691796517[/C][C]0.8226616406966[/C][C]0.4113308203483[/C][/ROW]
[ROW][C]92[/C][C]0.581000542014677[/C][C]0.837998915970645[/C][C]0.418999457985323[/C][/ROW]
[ROW][C]93[/C][C]0.537394154322133[/C][C]0.925211691355734[/C][C]0.462605845677867[/C][/ROW]
[ROW][C]94[/C][C]0.541726219421769[/C][C]0.916547561156462[/C][C]0.458273780578231[/C][/ROW]
[ROW][C]95[/C][C]0.570679579255285[/C][C]0.85864084148943[/C][C]0.429320420744715[/C][/ROW]
[ROW][C]96[/C][C]0.540063602893659[/C][C]0.919872794212682[/C][C]0.459936397106341[/C][/ROW]
[ROW][C]97[/C][C]0.523170413159012[/C][C]0.953659173681976[/C][C]0.476829586840988[/C][/ROW]
[ROW][C]98[/C][C]0.534136904881499[/C][C]0.931726190237002[/C][C]0.465863095118501[/C][/ROW]
[ROW][C]99[/C][C]0.599897126391641[/C][C]0.800205747216718[/C][C]0.400102873608359[/C][/ROW]
[ROW][C]100[/C][C]0.65449597055391[/C][C]0.691008058892179[/C][C]0.34550402944609[/C][/ROW]
[ROW][C]101[/C][C]0.620805881010832[/C][C]0.758388237978336[/C][C]0.379194118989168[/C][/ROW]
[ROW][C]102[/C][C]0.655252039423112[/C][C]0.689495921153776[/C][C]0.344747960576888[/C][/ROW]
[ROW][C]103[/C][C]0.617437757656494[/C][C]0.765124484687011[/C][C]0.382562242343506[/C][/ROW]
[ROW][C]104[/C][C]0.584665170616252[/C][C]0.830669658767497[/C][C]0.415334829383748[/C][/ROW]
[ROW][C]105[/C][C]0.538142259578312[/C][C]0.923715480843376[/C][C]0.461857740421688[/C][/ROW]
[ROW][C]106[/C][C]0.534471482965843[/C][C]0.931057034068314[/C][C]0.465528517034157[/C][/ROW]
[ROW][C]107[/C][C]0.503152001162926[/C][C]0.993695997674147[/C][C]0.496847998837073[/C][/ROW]
[ROW][C]108[/C][C]0.640785445001615[/C][C]0.71842910999677[/C][C]0.359214554998385[/C][/ROW]
[ROW][C]109[/C][C]0.660384127261802[/C][C]0.679231745476397[/C][C]0.339615872738198[/C][/ROW]
[ROW][C]110[/C][C]0.615991509396684[/C][C]0.768016981206633[/C][C]0.384008490603316[/C][/ROW]
[ROW][C]111[/C][C]0.587519202109909[/C][C]0.824961595780183[/C][C]0.412480797890091[/C][/ROW]
[ROW][C]112[/C][C]0.59026693831925[/C][C]0.8194661233615[/C][C]0.40973306168075[/C][/ROW]
[ROW][C]113[/C][C]0.554548481674745[/C][C]0.890903036650511[/C][C]0.445451518325255[/C][/ROW]
[ROW][C]114[/C][C]0.605132528946314[/C][C]0.789734942107373[/C][C]0.394867471053686[/C][/ROW]
[ROW][C]115[/C][C]0.560415644739345[/C][C]0.87916871052131[/C][C]0.439584355260655[/C][/ROW]
[ROW][C]116[/C][C]0.612984404418437[/C][C]0.774031191163126[/C][C]0.387015595581563[/C][/ROW]
[ROW][C]117[/C][C]0.567881350334964[/C][C]0.864237299330072[/C][C]0.432118649665036[/C][/ROW]
[ROW][C]118[/C][C]0.52332459603905[/C][C]0.9533508079219[/C][C]0.47667540396095[/C][/ROW]
[ROW][C]119[/C][C]0.514013665738145[/C][C]0.97197266852371[/C][C]0.485986334261855[/C][/ROW]
[ROW][C]120[/C][C]0.469598206359857[/C][C]0.939196412719715[/C][C]0.530401793640143[/C][/ROW]
[ROW][C]121[/C][C]0.44363302774394[/C][C]0.88726605548788[/C][C]0.55636697225606[/C][/ROW]
[ROW][C]122[/C][C]0.46080040445707[/C][C]0.92160080891414[/C][C]0.53919959554293[/C][/ROW]
[ROW][C]123[/C][C]0.414436189257742[/C][C]0.828872378515484[/C][C]0.585563810742258[/C][/ROW]
[ROW][C]124[/C][C]0.382373519000357[/C][C]0.764747038000714[/C][C]0.617626480999643[/C][/ROW]
[ROW][C]125[/C][C]0.380403766074364[/C][C]0.760807532148728[/C][C]0.619596233925636[/C][/ROW]
[ROW][C]126[/C][C]0.410266331737634[/C][C]0.820532663475267[/C][C]0.589733668262366[/C][/ROW]
[ROW][C]127[/C][C]0.483437928871308[/C][C]0.966875857742617[/C][C]0.516562071128692[/C][/ROW]
[ROW][C]128[/C][C]0.444261267775745[/C][C]0.88852253555149[/C][C]0.555738732224255[/C][/ROW]
[ROW][C]129[/C][C]0.389375933920003[/C][C]0.778751867840005[/C][C]0.610624066079997[/C][/ROW]
[ROW][C]130[/C][C]0.35424454796945[/C][C]0.7084890959389[/C][C]0.64575545203055[/C][/ROW]
[ROW][C]131[/C][C]0.346864945605917[/C][C]0.693729891211834[/C][C]0.653135054394083[/C][/ROW]
[ROW][C]132[/C][C]0.324223409840784[/C][C]0.648446819681567[/C][C]0.675776590159217[/C][/ROW]
[ROW][C]133[/C][C]0.278651509839712[/C][C]0.557303019679424[/C][C]0.721348490160288[/C][/ROW]
[ROW][C]134[/C][C]0.284282246963884[/C][C]0.568564493927768[/C][C]0.715717753036116[/C][/ROW]
[ROW][C]135[/C][C]0.413519399136123[/C][C]0.827038798272246[/C][C]0.586480600863877[/C][/ROW]
[ROW][C]136[/C][C]0.386634436931048[/C][C]0.773268873862095[/C][C]0.613365563068953[/C][/ROW]
[ROW][C]137[/C][C]0.364297651715356[/C][C]0.728595303430713[/C][C]0.635702348284644[/C][/ROW]
[ROW][C]138[/C][C]0.466994853362892[/C][C]0.933989706725783[/C][C]0.533005146637109[/C][/ROW]
[ROW][C]139[/C][C]0.462587395555066[/C][C]0.925174791110132[/C][C]0.537412604444934[/C][/ROW]
[ROW][C]140[/C][C]0.419286124379561[/C][C]0.838572248759122[/C][C]0.580713875620439[/C][/ROW]
[ROW][C]141[/C][C]0.434563276916997[/C][C]0.869126553833994[/C][C]0.565436723083003[/C][/ROW]
[ROW][C]142[/C][C]0.418189671375131[/C][C]0.836379342750263[/C][C]0.581810328624869[/C][/ROW]
[ROW][C]143[/C][C]0.600182492326221[/C][C]0.799635015347557[/C][C]0.399817507673779[/C][/ROW]
[ROW][C]144[/C][C]0.569613449545368[/C][C]0.860773100909263[/C][C]0.430386550454632[/C][/ROW]
[ROW][C]145[/C][C]0.617014285157193[/C][C]0.765971429685613[/C][C]0.382985714842807[/C][/ROW]
[ROW][C]146[/C][C]0.538481264142139[/C][C]0.923037471715722[/C][C]0.461518735857861[/C][/ROW]
[ROW][C]147[/C][C]0.465693933042954[/C][C]0.93138786608591[/C][C]0.534306066957046[/C][/ROW]
[ROW][C]148[/C][C]0.49523508833558[/C][C]0.99047017667116[/C][C]0.50476491166442[/C][/ROW]
[ROW][C]149[/C][C]0.515140776133505[/C][C]0.96971844773299[/C][C]0.484859223866495[/C][/ROW]
[ROW][C]150[/C][C]0.867326939996969[/C][C]0.265346120006062[/C][C]0.132673060003031[/C][/ROW]
[ROW][C]151[/C][C]0.813218933344877[/C][C]0.373562133310246[/C][C]0.186781066655123[/C][/ROW]
[ROW][C]152[/C][C]0.711712936966749[/C][C]0.576574126066501[/C][C]0.288287063033251[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]7.0173464509967e-63[/C][C]3.50867322549835e-63[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]6.55334077946684e-46[/C][C]3.27667038973342e-46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154624&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154624&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5334800123280320.9330399753439360.466519987671968
90.3746746820269950.7493493640539910.625325317973005
100.2412557909461960.4825115818923910.758744209053804
110.4979296564766080.9958593129532170.502070343523392
120.38647345223780.77294690447560.6135265477622
130.3884972105554560.7769944211109130.611502789444544
140.2965760227727040.5931520455454080.703423977227296
150.2291195237941030.4582390475882060.770880476205897
160.3106583234958890.6213166469917770.689341676504111
170.3234790588554060.6469581177108120.676520941144594
180.2538658295071970.5077316590143940.746134170492803
190.3467166335014820.6934332670029630.653283366498518
200.3015266575217130.6030533150434260.698473342478287
210.3928272651013680.7856545302027360.607172734898632
220.5122611031181170.9754777937637660.487738896881883
230.4701556531960890.9403113063921790.529844346803911
240.4193240989435190.8386481978870380.580675901056481
250.4468307944173860.8936615888347720.553169205582614
260.6131461219272470.7737077561455050.386853878072753
270.6758820536112260.6482358927775480.324117946388774
280.6241578532862640.7516842934274720.375842146713736
290.5910645801692540.8178708396614920.408935419830746
300.6001422266892070.7997155466215850.399857773310793
310.6371773573696120.7256452852607750.362822642630387
320.6672575459162090.6654849081675820.332742454083791
330.6493636640658850.701272671868230.350636335934115
340.6137416470858770.7725167058282470.386258352914123
350.6188623196630830.7622753606738340.381137680336917
360.6297969502216410.7404060995567180.370203049778359
370.6158024596729790.7683950806540420.384197540327021
380.6463295343632430.7073409312735140.353670465636757
390.6468810657266720.7062378685466560.353118934273328
400.6376153050313660.7247693899372670.362384694968634
410.6348922884532930.7302154230934140.365107711546707
420.6042160565170990.7915678869658020.395783943482901
430.603428243586840.793143512826320.39657175641316
440.600345991749340.799308016501320.39965400825066
450.6902792714188020.6194414571623950.309720728581198
460.6969663700996880.6060672598006240.303033629900312
470.6664821022633110.6670357954733770.333517897736689
480.6534794988537260.6930410022925470.346520501146274
490.6056634683728550.788673063254290.394336531627145
500.5979762753431980.8040474493136050.402023724656802
510.5592820583283060.8814358833433870.440717941671694
520.5602585760898960.8794828478202080.439741423910104
530.5732881708669290.8534236582661420.426711829133071
540.5461694474734940.9076611050530110.453830552526506
550.5247973215778570.9504053568442850.475202678422143
560.5201556803890120.9596886392219760.479844319610988
570.4874447546290250.974889509258050.512555245370975
580.4847889165190690.9695778330381380.515211083480931
590.4913590202824380.9827180405648750.508640979717562
600.4874986745731170.9749973491462350.512501325426883
610.4561242135406410.9122484270812820.543875786459359
620.4678149097388880.9356298194777760.532185090261112
630.4836901771466210.9673803542932410.516309822853379
640.4567536103314030.9135072206628050.543246389668597
650.4531932774584010.9063865549168010.546806722541599
660.4500070497577770.9000140995155550.549992950242223
670.4367509736428110.8735019472856210.56324902635719
680.4038286755694430.8076573511388860.596171324430557
690.3631468343900940.7262936687801890.636853165609906
700.4548218036332880.9096436072665770.545178196366712
710.5049592018197310.9900815963605380.495040798180269
720.459633783992820.919267567985640.54036621600718
730.418668960276430.837337920552860.58133103972357
740.5105554219741530.9788891560516940.489444578025847
750.4824241130254050.964848226050810.517575886974595
760.4796918678171610.9593837356343210.520308132182839
770.4380045906500420.8760091813000830.561995409349958
780.4271862981733360.8543725963466720.572813701826664
790.4537185982046080.9074371964092160.546281401795392
800.4215877999775790.8431755999551590.578412200022421
810.4724884613864250.944976922772850.527511538613575
820.547351644912820.905296710174360.45264835508718
830.5275796452054140.9448407095891720.472420354794586
840.5739879631018320.8520240737963370.426012036898168
850.5539702904233640.8920594191532720.446029709576636
860.5522603752126970.8954792495746060.447739624787303
870.64488108587520.7102378282496010.3551189141248
880.6318361171038130.7363277657923750.368163882896187
890.621103487122190.7577930257556190.37889651287781
900.6290449548444030.7419100903111940.370955045155597
910.58866917965170.82266164069660.4113308203483
920.5810005420146770.8379989159706450.418999457985323
930.5373941543221330.9252116913557340.462605845677867
940.5417262194217690.9165475611564620.458273780578231
950.5706795792552850.858640841489430.429320420744715
960.5400636028936590.9198727942126820.459936397106341
970.5231704131590120.9536591736819760.476829586840988
980.5341369048814990.9317261902370020.465863095118501
990.5998971263916410.8002057472167180.400102873608359
1000.654495970553910.6910080588921790.34550402944609
1010.6208058810108320.7583882379783360.379194118989168
1020.6552520394231120.6894959211537760.344747960576888
1030.6174377576564940.7651244846870110.382562242343506
1040.5846651706162520.8306696587674970.415334829383748
1050.5381422595783120.9237154808433760.461857740421688
1060.5344714829658430.9310570340683140.465528517034157
1070.5031520011629260.9936959976741470.496847998837073
1080.6407854450016150.718429109996770.359214554998385
1090.6603841272618020.6792317454763970.339615872738198
1100.6159915093966840.7680169812066330.384008490603316
1110.5875192021099090.8249615957801830.412480797890091
1120.590266938319250.81946612336150.40973306168075
1130.5545484816747450.8909030366505110.445451518325255
1140.6051325289463140.7897349421073730.394867471053686
1150.5604156447393450.879168710521310.439584355260655
1160.6129844044184370.7740311911631260.387015595581563
1170.5678813503349640.8642372993300720.432118649665036
1180.523324596039050.95335080792190.47667540396095
1190.5140136657381450.971972668523710.485986334261855
1200.4695982063598570.9391964127197150.530401793640143
1210.443633027743940.887266055487880.55636697225606
1220.460800404457070.921600808914140.53919959554293
1230.4144361892577420.8288723785154840.585563810742258
1240.3823735190003570.7647470380007140.617626480999643
1250.3804037660743640.7608075321487280.619596233925636
1260.4102663317376340.8205326634752670.589733668262366
1270.4834379288713080.9668758577426170.516562071128692
1280.4442612677757450.888522535551490.555738732224255
1290.3893759339200030.7787518678400050.610624066079997
1300.354244547969450.70848909593890.64575545203055
1310.3468649456059170.6937298912118340.653135054394083
1320.3242234098407840.6484468196815670.675776590159217
1330.2786515098397120.5573030196794240.721348490160288
1340.2842822469638840.5685644939277680.715717753036116
1350.4135193991361230.8270387982722460.586480600863877
1360.3866344369310480.7732688738620950.613365563068953
1370.3642976517153560.7285953034307130.635702348284644
1380.4669948533628920.9339897067257830.533005146637109
1390.4625873955550660.9251747911101320.537412604444934
1400.4192861243795610.8385722487591220.580713875620439
1410.4345632769169970.8691265538339940.565436723083003
1420.4181896713751310.8363793427502630.581810328624869
1430.6001824923262210.7996350153475570.399817507673779
1440.5696134495453680.8607731009092630.430386550454632
1450.6170142851571930.7659714296856130.382985714842807
1460.5384812641421390.9230374717157220.461518735857861
1470.4656939330429540.931387866085910.534306066957046
1480.495235088335580.990470176671160.50476491166442
1490.5151407761335050.969718447732990.484859223866495
1500.8673269399969690.2653461200060620.132673060003031
1510.8132189333448770.3735621333102460.186781066655123
1520.7117129369667490.5765741260665010.288287063033251
15317.0173464509967e-633.50867322549835e-63
15416.55334077946684e-463.27667038973342e-46






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 2 & 0.0136054421768707 & NOK \tabularnewline
5% type I error level & 2 & 0.0136054421768707 & OK \tabularnewline
10% type I error level & 2 & 0.0136054421768707 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154624&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]2[/C][C]0.0136054421768707[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0136054421768707[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0136054421768707[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154624&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154624&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 level20.0136054421768707NOK
5% type I error level20.0136054421768707OK
10% type I error level20.0136054421768707OK


Figures (Output of Computation)

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

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