EC 410 - Applied Regression Analysis and Forecasting

3 credit(s)
This course covers both methodology and practical applications of two powerful methods in business data analysis. In regression analysis, main aspects of multiple regression and logistic regressions are covered, including techniques in coding variables, variable selection, checking variable significance, dealing with multicollinearity, residual analysis, bootstrap, and ANOVA. In forecasting, major methods such as time series decomposition, exponential smoothing and ARIMA modeling are covered. Practical aspects in data preprocessing and diagnostics, model selection, assessment and interpretation are emphasized for both topics, as well as implementation using suitable programming language and/or software.
Prerequisite(s): QNT 230 or M 260.

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