SAS Predictive Modeling - (3 in 1) with 3 live Case Studies

This lecture explains linear regression, including simple and multiple forms, independent variables, the line of best fit, and the standard equation; it covers adjusted r-squared, f-statistic, and p-values.

Apply practical linear regression to predict customer satisfaction using significant variables such as product quality, price flexibility, and packaging, validate predictions on training and validation data.

Set the cut point probability level in logistic regression to convert predicted probabilities into binary outcomes (0/1), using a credit risk example to distinguish events from non-events.

Explore logistic regression for a binary loan repayment outcome, interpreting odds and odds ratios, evaluating models with confusion metrics and roc curves while applying a credit dataset and case study.

Explore logistic regression in credit risk analytics, three live case studies, selecting significant variables via stepwise methods, estimating maximum likelihood, and predicting loan default probabilities with SAS Proc Logistic.

Explore the components of time series analysis, including secular trend, cyclical, seasonal, and random fluctuations, and learn how these elements shape forecasting and business planning.

Explore arima modelling for time series analysis, focusing on making data stationary through transformations and differencing, and combining autoregressive and moving average components to forecast future values.

Practice time series modeling with an airline dataset containing date and passengers, test stationarity, and forecast the next 12 months using SAS procedures.

Apply time series methods to predict passenger travel for the next two months using differencing, stationarity checks, and hypothesis testing in SAS predictive modeling.