STAT 292 Statistics for the Mathematical Sciences II (3)
Further topics in probability and statistics: two-sample estimation and
hypothesis testing, analysis of variance, categorical data, simple and multiple linear regression,
and nonparametric techniques. (Offered spring semester only.)
STAT 291 Statistics for Mathematical Sciences I
- Familiarity with basic statistical distributions.
- Understanding of one-sample estimation and hypothesis testing.
- Ability to use the computer to perform statistical analyses.
| Required Course Materials:
J. Devore, Probability and Statistics for Engineering and the Sciences,
8th edition, Cengage, 2012 (ISBN: 9780538733526)
L. Marlin Eby, Ph.D., Professor of Mathematics and Statistics
- Students majoring in mathematics or computer science.
- Students wanting a more rigorous second statistics course.
- This course satisfies the second course statistics/computer science
requirement for mathematics majors and can be used to meet the elective
requirement for computer science majors.
- To use probability as the bridge between descriptive and inferential analysis.
- To intuitively understand each concept.
- To understand, when possible and appropriate, the rigor of a mathematical
- 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 nonquantative 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.
- Two-Sample Interval Estimation: properties and assumptions; confidence
intervals for the difference between two means and proportions and the ratio
of two variances (standard deviations); and sample size determination.
- Two-Sample Hypothesis Testing: properties and assumptions; tests on the
difference between two means and proportions and the ratio of two variances
(standard deviations); power calculations; relationship to interval
estimation; and sample size determination.
- Analysis of Variance: properties and assumptions, one-way and partial and
complete two-way ANOVA's, and multiple comparisons.
- Nonparametric Inference: two-sample estimation and hypothesis testing for
the difference between two means.
- Simple Linear Regression: properties and assumptions, fitting the model
by least squares, descriptive analysis, hypothesis tests, and confidence and
- Linear Correlation Coefficient: properties and assumptions, descriptive
analysis, hypothesis tests, confidence interval, and nonparametric
- Multiple Linear Regression: properties and assumptions, fitting the model
by least squares, descriptive analysis, hypothesis tests, confidence and
prediction intervals, model building, standardized regression, and
regression using dummy variables.
- Analysis of Categorical Data: test on multinomial proportions,
goodness-of-fit test, and test of independence (test on the differences
among two or more multinomial proportion vectors).
Revised: October 2013 (textbook); August 2010