Overview
Description
Theory and application of statistical inference with special emphasis on multivariate models, including multiple and partial regression, factor analysis, path analysis and discriminant function analysis.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring - Even Years
Terms
spring
Attributes
Standard
Learning Outcomes
- design and create cutting-edge-quality graphics/visualizations (including simulations) and tables for all the statistics in the course using spreadsheets, statistical packages and special purpose programming.
- report and interpret all the statistics in the course in writing and in presentation format at a graduate level.
- translate a research problem into statistical terms); formulate hypotheses; evaluate alternative candidate statistics with respect to their precision, their robustness, and their compliance with assumptions, especially concerning distributions of residuals. Distinguish serious and trivial consequences of assumption violation. Defend their choice.
- identify central tendency and dispersion components in the formulas of all the statistics in the course.
- compute statistics in the regression family (OLS, WLS, GLS, and multi-level analysis), factor analysis (exploratory and confirmatory) and principal components, and beginning cluster analysis and discriminant analysis using a statistical package; identify relevant estimates in output of a statistical package; identify implications of the results for the hypotheses; estimate and interpret the magnitudes of associations using both estimates, first differences and confidence intervals.