Regression Analysis of Dependent Variables - 1183 Words

Table: 1, represents the results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R ² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R ² values in the analysis are fairly good which is more than 60%, indicates the considered model is fit for analysis. Also, the F-Statistics which provides the statistical significance of the model and its probabilities which are below 5% level and hence proves the model’s significance. Table: 1: Regression Results. Method: Least Squares Sample: 2005Q1 2013Q4 Included observations: 36 R-squared Adjusted R-squared F-statistic Prob(F-statistic) 0.955378 0.946146 103.4845 0.00000 0.963182 0.955564 126.4426 0.00000 0.746736 0.90889 15.58318 0.01877 0.952115 0.942208 96.10377 0.00000 0.960883 0.95279 118.7272 0.00000 0.868418 0.841194 31.89909 0.00000 0.87641 0.85084 34.27454 0.00000 0.933336 0.919543 67.66915 0.00000 0.889215 0.866294 38.79462 0.00000 0.924163 0.908473 58.89987 0.00000 0.739903 0.68609 13.74949 0.00000 Serial Correlation and Heteroskedasticity: Normally the possibilities for the time series data to have the Serial correlation or auto correlation are more. It can be tested with theShow MoreRelatedRegression Analysis of American Hotels Having Price as Dependent Variable7488 Words   |  30 Pagesmore or less linear way. To say it differently, price differences between hotels underscore the presence or not of some variables expected to influence the latter. 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