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

Topics:

  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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