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SST 301 Programming Language for Statistics Paper

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BSc. Statistics and Programming SST 301: Programming Language for Statistics I By Omari C.O Prerequisite: SST 205 Course Purpose: To provide the students with knowledge and skills necessary for implementing most commonly used statistical analysis. Emphasis will be on matrix formulations and the use of statistical packages such as R, S-plus, SAS, Matlab etc. Expected Learning Outcomes: At the end of the course, students should be able to: 1. Fit linear regression models using R software and be able to interpret various outputs. 2. Perform model diagnostics through residual plots, influential observation identification, and collinearity detection using appropriate statistical softwares. 3. Apply data transformation techniques on data sets in appropriate statistical softwares. Course Content Introduction to a statistical software interface and how to handle linear models and then their application to empirical data. The general linear model (GLM procedures); ordinary least squares estimation, diagnostics, including departures from underlying assumptions, detection of outliers, effects of influential observations, and leverage; analysis of variance, including one-way and twoway layouts; analysis of covariance; polynomial and interaction models; weighted-least squares and robust estimation; model fitting and validation. Instructional Materials and/or Equipment Whiteboard, LCD/Overhead Projector, Handouts, Laptop. Course Assessment The assessment will be conducted by CATs (Continuo ...
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