Sampling Distributions and the Central Limit Theorem
Confidence Intervals (I)
Confidence Intervals (II)
Hypothesis Tests (I)
Hypothesis Tests (II)
Comparison of Two Populations
Analysis of Variance a. Introduce Design of Experiments
Nonparametric Statistics
Introduction to regression methods
Several regressors; Scatter plot matrix
Anscombe’s data sets
Problem of insignificance of important regressors
Ridge regression; Dummy variables; Transformations – Power transformation, Box-Cox transformation
Heteroscedasticity; Possible causes; Detection – graphical methods, formal tests; Remedies – Transformations, Adjustment to standard errors of OLS estimates, Generalized least squares
Autocorrelation; Possible causes; Detection – graphical methods, formal tests; Remedies – First differences, Adjustment to standard errors of OLS estimates, Generalized least squares, Dummy variables and autocorrelation, forecasting in the presence of autocorrelation
Big data Regression analysis- R and Hadoop
Binary response; Linear Probability Model; Advantages and issues; Guidelines for Linear
