Advanced Statistics for Internet Research

About

This course covers multiple and logistic regression techniques. It shows how to assess the adequacy of a model using supporting graphical tools both before and after the regression, and by analysis of residuals. It explores diagnostics and corrective techniques for the four major data problems: outliers, collinearity, heteroscedasticity and non-linearity. This course covers the interpretation of coefficients and odds ratios, measures of fit, supporting graphics, and diagnostics and corrective techniques for common problems, principal components analysis, construction and interpretation of components, as well as contrasts to other techniques like item analysis.  

Learning Objectives

• Understand the strengths and limitations of multiple regression; 
• Understand and interpret multiple regression coefficients and significance levels, for both continuous and categorical independent variables; 
• Understand and interpret fit statistics for models; 
• Be able to diagnose and correct the four major problems with regression: outliers, collinearity, heteroscedasticity and nonlinearity. 
• Understand the strengths and limitations of logistic regression; 
• Understand and interpret logistic regression coefficients, odds ratios and significance levels, for both continuous and categorical independent variables; 
• Understand and interpret fit statistics for models; 
• Be able to diagnose and correct the two major problems with logistic regression: outliers and collinearity; 
• Understand different measures of similarity and dissimilarity; 
• Understand and interpret eigenvalues, communalities, factor loadings, and rotations for principal components analysis; 
• Understand the relation between principal components, ordinary regression and logistic regression.