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Topics include review of statistics; F and chi-squared distributions ; review of simple linear regression model; multiple linear regression model; hypothesis testing, forecasting, diagnostics with regression models (including heteroskedasticity, serial correlation and model specification). Examples drawn from economics, finance, accounting, marketing and management.
Intended learning outcomes
- Apply the least-squares method of estimation to the context of the simple linear regression model.
- Apply the principles of the least-squares method of estimation and inference to the multiple linear regression model.
- Apply the statistical software R to estimate, test hypotheses and forecast in the context of the linear regression model.
- Explain various problems that arise from applying the linear regression model to data, including multicollinearity, specification errors, heteroskedasticity, nonstationarity and autocorrelation.
High level of development: written communication; collaborative learning; problem solving; team work; statistical reasoning; application of theory to practice; interpretation and analysis; critical thinking; synthesis of data and other information; use of computer software.
Moderate level of development: oral communication; evaluation of data and other information; accessing data and other information from a range of sources; receptiveness to alternative ideas.
Last updated: 22 January 2022