1. Time Series. Forecasting with ARIMA models
Characteristics of a time series: Frequency, trend and seasonal cycle.
Concept of a stationary time series
ACF an PACF
Autoregressive models AR (p)
Moving average models MA (q)
ARMA and ARIMA models
Estimation and diagnosis.
Seasonal ARIMA models : identification, diagnosis and prediction.
2. Logistic regression.
Logit Model Overview.
Interpretation of the parameters.