Second Year Modules

 

 

STAS211: Probability, Distribution Theory and Estimation 

Stream: BCom - Statistics

Credits: 20

 

Pre-requisites

STAV102

 

Module Content

  • Probability concepts: Experiment, sample space, events.  
  • Computing probabilities: Permutations and Combinations. 
  • Conditional Probability. 
  • Bayes’ Theorem. 
  • Functions associated with random variables. 
  • Special Discrete Distributions: Bernoulli, Binomial, Hypergeometric, Poisson and Negative Binomial. 
  • Special Continuous Distributions: Uniform, Exponential, Gamma, Chi-Squared, Beta and Normal Statistical Inference: Estimation and hypothesis testing.  

 

 

 

 

STAS201: Theory of Distribution

Stream: BSc - Mathematical Statistics

Credits: 20

Pre-requisites

STAS101 and STAS102 and MATT102

 

Module Content

  • Basic probability concepts. 
  • Theory of continuous probability distributions. 
  • Expected values and MGF. 
  • Special continuous probability distributions: Uniform, Gamma, Exponential, Weibull, Pareto and Normal. 
  • Theory of multivariate discrete and continuous distributions, marginal and conditional distributions. 
  • Co-variance and correlation. 
  • Theory of conditional expectation and conditional variance. 
  • Functions of random variables: distribution function technique, transformation methods, MGF technique. Limiting distributions and stochastic convergence. 
  • CLT and applications. 
  • Sampling distributions: Chi-square, t, F, Beta distributions. 
  • Monte Carlo simulation. 

 

 

 

 

STAS202: Regression Analysis and Advanced Regresion Topics

Stream: BCom - Statistics & BSc - Mathematical Statistics

Credits: 30

 

Pre-requisites

  • MATS101, MATS102 and STAV102 OR  
  • STAS101, STAS102 and MATT102 OR  
  • STAV102 and MATT102 

Module Content

  • Simple linear regression: the theoretical (mathematical) model, assumptions, estimation, coefficient of determination, prediction, regression through the origin.  
  • Multiple linear regressions: the model, assumptions, estimation, inferences.  
  • Polynomial regression, second and higher order regression.  
  • Testing portions of a model.  
  • Stepwise regression.  
  • Models with qualitative and quantitative predictors.  
  • Model building.  
  • Regression pitfalls.  
  • Residual analysis.  
  • Piecewise regression.