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STAT 361 Applied Methods in Statistics I Units: 3.00
A detailed study of simple and multiple linear regression, residuals and model adequacy. The least squares solution for the general linear regression model. Analysis of variance for regression and simple designed experiments; analysis of categorical data.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Requirements: Prerequisite (MATH 110 or MATH 111 or MATH 112) and (STAT 252 or STAT 268 or STAT 351) and (STAT 263 or STAT 269) or permission of the Department.
Exclusion ECON 351.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Applying analysis of variance to understand the sources of uncertainty; applying least square criterion to build fitted models.
- Conducting data analysis using linear regression models and reporting the analysis results.
- Creating scatter plots, boxplots, pairwise plots to explore the data; analyzing data using linear regression models; obtaining least square estimates; comparing two different linear regression models; choosing significant variables; performing regression diagnostics for assessing the adequacy of models; drawing conclusions based on the analysis results.
- Evaluating the appropriateness of using linear regression models in real applications.
- Using built-in functions in R software to perform linear regression analysis.
- Writing independent functions and codes in R to conduct linear regression analysis.