MTH202 Probability and Statistics
MTH202: Probability and statistics
• Recapitulation: Counting (urn, coins, cards).
• Axiomatic approach to probability, conditional probability, independence of events.
• Discrete random variables, probability mass function, some standard discrete distributions and
examples.
• Continuous random variables, probability density function, some standard continuous distributions
and examples.
• Bivariate distributions (discrete and continuous), marginal and conditional distributions,
covariance, correlation coefficient.
• Moments, Markov’s inequality, Chebychev’s inequality.
• Sums of independent random variables, law of large numbers, central limit theorem
• A glimpse into estimation theory (maximum likelihood estimation, method of moments) and testing
of hypothesis.
Recommended Reading
• K. L. Chung and F. AitSahila, Elementary Probability Theory, Springer (2004).
• W. Feller, Introduction to Probability and Statistics, Wiley (1956).
• S. Ross, A First Course in Probability, Pearson Education Inc. (2006).
• R. Isaac, The Pleasures of Probability, Springer (Undergraduate Texts in Mathematics) (1995).