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Q,3 Use the Excel file “Time_Series.xlsx”. Develop a time series model basedon the data. Provide the process of your model developmentTime series PlotGretl software (WEKA) output fileMODEL-1 ARIMA Model 1: ARMAX, using observations 1:01-22:17 (T = 521)Dependent variable: V1Standard errors based on HessianCoefficientStd. Errorzp-valueconst-0.6477140.175359-3.69360.00022***phi_10.5133560.041564812.3507<0.00001***theta_10.6898320.032964420.9265<0.00001***hour-0.0008259560.000581344-1.42080.15538Mean dependent var-0.865611S.D. dependent var1.008562Mean of innovations-0.000422S.D. of innovations0.579599Log-likelihood-455.8833Akaike criterion921.7665Schwarz criterion943.0453Hannan-Quinn930.1015RealImaginaryModulusFrequencyARRoot 1 1.94800.00001.94800.0000MARoot 1 -1.44960.00001.44960.5000SOURCE: Computed data Output file Gretl software ( WEKA) output file
Source : WEKA gretl output file – Test statistics of normality
Q.1 A) Develop a linear regression model with variable cnt as the dependentvariable and the other variables as independent variables (you mayselect some of them). Provide the process of your model development indetail.Linear Regression model Model SummaryModelRR SquareAdjusted RSquareStd. Error of theEstimate1.628a.395.393141.624a. Predictors: (Constant), windspeed, temp, holiday, weathersit, yr, hr, workingday, mnth, hum, season, dtedayANOVAaModelSum of SquaresdfMean SquareFSig.1Regression52213813.305114746710.300236.657.000bResidual79988564.339398820057.313Total132202377.6443999a. Dependent Variable: cntb. Predictors: (Constant), windspeed, temp, holiday, weathersit, yr, hr, workingday, mnth, hum, season, dtedayCoefficientsaModelUnstandardized CoefficientsStandardizedCoefficientstSig.BStd. ErrorBeta1(Constant)-61260.74540369.191-1.518.129dteday4.534E-006.000.4091.517.129season15.6714.688.0913.343.001yr-52.24494.382-.143-.554.580mnth-8.9557.838-.159-1.143.253hr7.817.350.29422.352.000holiday-24.59814.719-.021-1.671.095workingday3.8804.974.010.780.435weathersit-8.1123.995-.028-2.031.042temp255.92913.361.27219.155.000
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