Statistics for Risk Modeling

Catalog description

Statistical techniques in business forecasting such as: simple linear regression, multiple linear regression, hypothesis testing and confidence intervals in linear regression models, testing of models, data analysis and appropriateness of models, linear time series models, moving average, regression-based and/or ARIMA models, estimation, data analysis and forecasting with time series models, forecast errors and confidence intervals, classification, clustering and tree-based methods. Emphasizes analysis of various types of real data using R.