Estadistica Aplicada a las CCSS 2

Forecasting Time Serires with ARIMA Models Logit

LEARNING OUTCOMES 3. Have the ability to collect and interpret data and information on which to base their conclusions including, when necessary and relevant, reflection on social, scientific or ethical matters in the field of Sociology. GENERAL COMPETENCES 1. Analyse and interpret social reality following the scientific method, from a critical perspective and recognizing the plurality of theoretical-methodological approaches of the fields of specialty and subdisciplines related to Sociology. SPECIFIC COMPETENCES 12. Know the concepts and statistical techniques applied to human society.

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 White noise Autoregressive models AR (p) Moving average models MA (q) ARMA and ARIMA models Estimation and diagnosis. Forecasting Seasonal ARIMA models : identification, diagnosis and prediction. 2. Logistic regression. Logit Model Overview. Parameter estimation. Interpretation of the parameters. Model diagnose 3. Extensions

Theory (4ECTS). Lectures with support material available via web. Practices (2ECTS) Classes in computer classroom. Debates.