Statistics and parameters. Properties for a statistic. Central Limit Theorem. Distribution of the sample mean, difference in means and the proportion. Point and interval estimates for the mean, difference in means, and proportion. Hypotheses testing and types of errors. Significance levels and p values. Small sample testing: Chi square, t and F distributions and their properties. Applications of chi square and t distributions to interval estimates and tests. UNIT 2 : CLASSICAL TWO VARIABLE LINEAR REGRESSION MODEL Types of Data : Time Series, Cross Section and Panel Data. Concept of PRF and SRF. Estimation of the SRF using OLS. Analysis of variance and R squared. Understanding the residuals/error term. Assumptions of the model. Expectation and standard errors of the regression coefficients and the error term. Gauss Markov Theorem. Confidence intervals and tests on population regression coefficients, variance of population disturbance term, and forecasts. Testing the significance of the model as a whole. Testing the normality assumption. UNIT 3 : MULTIPLE REGRESSION MODEL The three variable case. Derivation of the coefficients. Correlation. Additional assumptions. Adjusted R square. Confidence intervals and testing of the regression coefficients. F and t tests for structural stability, contribution and justification of an explanatory variable. UNIT 4 : OTHER FUNCTIONAL FORMS Regressions in deviation form and through the origin. The log-log, log-lin, lin-log, reciprocal, log-reciprocal models with application. UNIT 5 : DUMMY VARIABLES Intercept, Slope Dummy variables. Interaction between qualitative variables. Interaction between quantitative and qualitative variables. Dummies for testing for the presences of Seasonal Trends. Main Readings 1. Christopher Dougherty, Introductory Econometrics 3rd Edition Oxford University Press (2007) 2. Gujarati , Damodar : Basic Econometrics , 3rd edition Mc.Graw Hill, New

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