People Innovation Excellence

Probability and Statistics (2 Credits)

Learning Outcomes:

Compute conditional probabilities directly and using Bayes’ theorem, and check for independence of events; Understand the law of large numbers and the central limit theorem and Compute the covariance and correlation between jointly distributed variables; Construct estimates and predictions using the posterior distribution; Use null hypothesis significance testing (NHST) to test the significance of results, and understand and compute the p-value for these tests; Use specific significance tests including, z-test t-test (one and two sample), chi-squared test; Compute and interpret simple linear regression between two variables


  1. Counting
  2. Random variables, distributions, quantiles, mean variance
  3. Conditional probability, Bayes’ theorem, base rate fallacy
  4. Joint distributions, covariance, correlation, independence
  5. Central limit theorem; Bayesian inference with known priors, probability intervals
  6. Conjugate priors
  7. Bayesian inference with unknown priors
  8. Frequentist significance tests and confidence intervals
  9. Resampling methods: bootstrapping
  10. Linear regression

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