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Statistics for Social Sciences II: Multivariate Techniques
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Other Subject Area: International Relations
Instruction in: English
Course Code: 16623
Transcript Source: Partner Institution
Course Details: Level 200
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Statistics for Social Sciences I or a similar introductory statistics course.
DESCRIPTION
Topic 1. Linear regression.
1.1. Linear regression. Introduction; simple and multiple regression; motivation; graphical data analysis; model formulation; dummy variables; parameter interpretation; examples; applications.
1.2. Fitting the model to the data; the least squares criterion; using the fitted model.
1.3. Model assumptions; inference on model parameters I: confidence intervals; inference on the response.
1.4. Inference on model parameters II: hypothesis testing; statistical significance of estimated parameters.
1.5. Assessing model fit; ANOVA.
1.6. Selection of predictor variables; multicollinearity; model diagnostics; model validation.
Topic 2. Binomial logistic regression.
2.1. Motivation; model assumptions and formulation; parameter interpretation; examples; applications.
2.2. Fitting the model to the data; using the fitted model; inference on model parameters; statistical significance of estimated parameters.
2.3. Assessing model fit; selection of predictor variables; multicollinearity.
Topic 3. Principal component analysis.
3.1. Motivation; formulation; variance explained; examples; applications.
3.2. Deciding how many components to keep; component scores; interpretation of components; graphical representations.
Topic 4. Cluster analysis.
4.1. Motivation; k-means clustering.
4.2. Hierarchical methods; similarity measures; dendrograms.
4.3. Applications and examples.
1.1. Linear regression. Introduction; simple and multiple regression; motivation; graphical data analysis; model formulation; dummy variables; parameter interpretation; examples; applications.
1.2. Fitting the model to the data; the least squares criterion; using the fitted model.
1.3. Model assumptions; inference on model parameters I: confidence intervals; inference on the response.
1.4. Inference on model parameters II: hypothesis testing; statistical significance of estimated parameters.
1.5. Assessing model fit; ANOVA.
1.6. Selection of predictor variables; multicollinearity; model diagnostics; model validation.
Topic 2. Binomial logistic regression.
2.1. Motivation; model assumptions and formulation; parameter interpretation; examples; applications.
2.2. Fitting the model to the data; using the fitted model; inference on model parameters; statistical significance of estimated parameters.
2.3. Assessing model fit; selection of predictor variables; multicollinearity.
Topic 3. Principal component analysis.
3.1. Motivation; formulation; variance explained; examples; applications.
3.2. Deciding how many components to keep; component scores; interpretation of components; graphical representations.
Topic 4. Cluster analysis.
4.1. Motivation; k-means clustering.
4.2. Hierarchical methods; similarity measures; dendrograms.
4.3. Applications and examples.
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