Introductory Courses for Undergraduate Students

Introductory Applied Statistics for the Life Sciences

Course Number: 
371
Credits: 
3
Frequency: 
Fall, Spring, Summer
Course Description: 
The course will provide students in the life sciences with an introduction to modern statistical practice. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.
Course Prerequisites: 
Math 112 & 113 or Math 114
Course Audience: 
Students in many disciplines in the life sciences such as biology, biochemistry, genetics, and kinesiology.

Introductory Applied Statistics for Engineers

Course Number: 
324
Credits: 
3
Frequency: 
Fall, Spring
Course Description: 
Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design.
Course Prerequisites: 
Math 222
Course Audience: 
Students in engineering.

Accelerated Introduction to Statistical Methods*

Course Number: 
302
Credits: 
3
Frequency: 
Fall, Spring
Course Description: 
Graphical and numerical exploration of data; standard errors; distributions for statistical models including binomial, Poisson, normal; estimation; hypothesis testing; randomization tests; basic principles of experimental design; regression; ANOVA; categorical data analysis; goodness of fit; application.
Course Prerequisites: 
Math 221
Course Audience: 
Students majoring in math or statistics or those wishing to take additional statistics courses.

Introduction to Statistical Methods

Course Number: 
301
Credits: 
3
Frequency: 
Fall, Spring, Summer
Course Description: 
Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.
Course Prerequisites: 
Completion of Quantitative Reasoning A. This is suitably met with a course in college algebra via Math 112 or Math 114.
Course Audience: 
Students in many disciplines including the social sciences, nursing, and social welfare. Students majoring in math or statistics should enroll in Stat 302.
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