STAT 269 Introductory Statistics (3)
Descriptive measures, normal distributions, one-sample and two-sample
hypothesis testing and estimation, correlation, and analysis of variance. Meets General Education Mathematical Sciences requirement. (Offered each semester.)
- Some familiarity with quantitative topics and problem solving.
- Some experience in using a computer to solve problems.
| Required Course Materials:
Ron Larson and Betsy Farber, Elementary
Statistics: Picturing the World, 4th edition, Pearson, 2009 (ISBN: 9780132424332)
L. Marlin Eby, Ph.D., Professor of Mathematics and Statistics
Students majoring in athletic training, biochemistry, biology,
biopsychology, chemistry, economics, environmental education, environmental
science, environmental studies, family and consumer sciences education, human
development and family science, nursing, nutrition and dietetics, pre-med,
pre-physical therapy, psychology, or social work.
- To become familiar with both descriptive and inferential analyses.
- To use probability as the bridge between descriptive and inferential
- To intuitively understand each concept.
- To integrate topics by identifying commonalties.
- To understand the limitations of each analysis through consideration of
- To express general concepts in terms of the application.
- To communicate results, clearly and completely, in a manner appropriate
to nonquantitative audiences.
- To be introduced to the computer's capabilities in solving practical
problems, using the computer for analysis only after understanding how to
perform the analysis manually.
- Descriptive Statistics: pictorial and tabular methods; measures of
location, variability, and position.
- Normal Distributions: percentages and percentiles for normal populations,
and Central Limit Theorem.
- One-Sample Interval Estimation: properties and assumptions, confidence
intervals for a mean and proportion, and sample size determination.
- One-Sample Hypothesis Testing: properties and assumptions, tests on a
mean and proportion, and relationship to interval estimation.
- Two-Sample Interval Estimation: properties and assumptions, confidence
intervals for the difference between two means and proportions.
- Two-Sample Hypothesis Testing: properties and assumptions, tests on the
difference between two means and proportions and the ratio of two variances
(standard deviations), and relationship to interval estimation.
- Linear Correlation: properties and assumptions, descriptive analysis, and
- Analysis of Variance: properties and assumptions, and test for
differences among two or more means.
Reviewed: September 2011