This is a list of all courses offered by the Math Department.  Not all courses are offered each year.  What is provided is a general description of the courses and the prerequisites.  The actual content may vary.

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Description

STAT 430 will introduce modern techniques of computational statistics for practical analysis of data. The course will utilize the SAS software system, which is widely used both in statistical applications and in corporate data management applications. Data analysis and interpretation will be emphasized, rather than statistical theory. At every point in the course real world data sets will be used to illustrate statistical principles.

For undergraduates in mathematics or computer science seeking statistical employment after graduation, SAS is a valuable job credential. For graduate students in fields which use statistics heavily, such as agriculture, life science, education, engineering or social science, SAS is a powerful aid to research.  Graduate students who have successfully taken an applied statistics course in their own department should have no difficulty with STAT 430.

Prerequisites

1 course with a minimum grade of C- from (STAT400, STAT410).


Level of Rigor

Standard


Sample Textbooks

Applied Statistics and the SAS Programming Language, by R.P. Cody and J.K. Smith

The Little SAS Book: A Primer, by L. Delwiche & S. Slaughter


Applications



If you like this course, you might also consider the following courses



Additional Notes

Students interested in grad school in STAT should consider this course 

Topics

Brief review of statistical concepts

Descriptive statistics

Points estimators

Confidence intervals

Hypothesis testing

Introduction to the SAS software system

Data screening

Data summaries and graphics

Creating and manipulating data sets

Merging, sorting and splitting data sets

Least squares and fitting models

Straight line fits

Graphical tools for model criticism

Multiple predictors

Categorical predictors and analysis of variance

Categorical data

Frequency tables

Logistic regression

Models for count data

Simulation