Course Details:

Statistics: Review of statistics, describing data – frequency tables, graphs, and summarizing data – measures of central tendency and variation, correlation coefficient.

Probability: Probability models and axioms, conditioning and Bayes' rule, Independence, random variables, standard probability density functions, covariance, correlation, functions of random variables, moment generating function, properties of normal distribution, Weak law of large numbers, central limit theorem.

Statistical Inference: Parameter estimation – maximum likelihood estimation (mle), method of moments, Bayes’ estimator, tests of hypotheses tests for mean and variance, t-test, chi-square test, evaluation of point estimators – unbiasedness, mean squared error (mse).


Text Books:

  1. Sheldon M. Ross, Introduction to Probability and Statistics for Engineers and Scientists, Academic Press.

Reference Books:

  1. Kandathody M. Ramachandran and Chris P. Tsokos, Mathematical Statistics with Applications, Academic Press,
  2. A. Popoulis and S. Pillai, Probability, Random Variables and Stochastic Processes, McGraw Hill Education; 4th Edition.