Contents

  • Random Variables, Probability Distributions
  • Properties of Normal Distribution
  • 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
  • Regression Models for count data
  • Missing Value Analysis
  • Survival Analysis
  • Design of Experiments
  • Course Content of Forecasting Analytics
  • Data Mining -1 (Unsupervised Learning)
  • Data Mining-2 (Supervised Learning)

Duration : 45 Hrs

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