Intended as the first graduate-level course in statistics for
quantitatively oriented students from disciplines in 
experimental science with some prior exposure to introductory 
statistics. Upon completion of the course, students will 
understand the paradigm of statistical inference about a 
population through statistical analysis of sampled data. They 
will learn how to compute, visualize and interpret basic 
descriptive statistics. They will also understand basic 
probability and how to apply it, and will know the definition, 
interpretation, and use of probability distributions, including 
the normal and binomial distributions. Students will be trained 
in methods of inference about means and proportions based upon 
one- and two-sample data.  They will be introduced to the one-
way analysis of variance model and methodology. Students will 
also learn bivariate data analysis, including correlation, 
simple and multiple linear regression, and chi-square tests for 
two-way tables. They will know how to apply linear regression 
and one-way analysis of variance for the purpose of data 
analysis to their own research problems using the statistical 
software package SAS.

This course will cover principles of sampling, descriptive
statistics, probability and probability distributions, binomial
and normal distributions, sampling distributions, point
estimation and confidence interval, hypothesis testing about
means and proportions for one and two samples, one-way analysis
of variance, correlation, simple and multiple linear regression,
and categorical data analysis.