Claim up to $500 in flight credits!
Get up to $500 in flight credits or grants toward study or internship programs abroad when you apply by January 01, 2025. See our Official Rules for full details.
Statistics for Social Sciences II: Multivariate Techniques
Engineering & Social Sciences Program
Madrid, Spain
Dates: 8/28/22 - 12/23/22
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.