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Course: 2019/2020

Statistics for social sciences III

(13035)

Study: Bachelor in Sociology (208)

Coordinating teacher: MOLINA PERALTA, ISABEL

Department assigned to the subject: Department of Statistics

Type: Electives

ECTS Credits: 6.0 ECTS

Course: 4º

Semester:

Students are expected to have completed

Estadistica Aplicada a las CCSS 2

Competences and skills that will be acquired and learning results. Further information on this link

Forecasting Time Serires with ARIMA Models Logit

Description of contents: programme

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

Learning activities and methodology

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

Assessment System

There will be a midterm exam. If the exam grade is greater than 5, the student does not need to take the final exam of this part. For these students the final grade is the average between the midterm exam and the final exam (only the second part of the course)

Students who do not pass the midterm exam with more than 6  will take both parts at the final exam. The final grade will be calculated in the most favorable to the student: 50% control and 50% exam or  exam grade only.

% end-of-term-examination 50
% of continuous assessment (assigments, laboratory, practicals...) 50

Basic Bibliography

Peña, D. Análisis de Series temporales. Alianza. 2005

The course syllabus and the academic weekly planning may change due academic events or other reasons.