

Course ID:  STAT 6315. 4 hours. 
Course Title:  Statistical Methods for Researchers 
Course Description:  Basic statistical methods through one and twosample
inference, regression, correlation, oneway analysis of
variance, analysis of covariance, and simple methods of
categorical data analysis. Course emphasizes implementation and
interpretation of statistical methods. Statistical software
(SAS) is integrated into the course. 
Oasis Title:  Statistical Methods Researcher 
Duplicate Credit:  Not open to students with credit in STAT 6210, STAT 6210E, STAT 6310, STAT 6315E 
Prerequisite:  Permission of department 
Semester Course Offered:  Offered fall, spring and summer semester every year. 
Grading System:  AF (Traditional) 

Course Objectives:  Intended as the first graduatelevel 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 twosample 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 chisquare tests for
twoway tables. They will know how to apply linear regression
and oneway analysis of variance for the purpose of data
analysis to their own research problems using the statistical
software package SAS. 
Topical Outline:  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, oneway analysis
of variance, correlation, simple and multiple linear regression,
and categorical data analysis. 