Course Title: Introduction to Statistics
Campus Course Code: MATH 6301
Campus: UT Permian Basin
Program: Non-Program
Description
This course is designed for graduate students who require a basic understanding of statistics but have not previously had a statistics course. The course covers basic descriptive statistics, elementary probability, one- and two-population mean and variance comparisons, and an introduction to ANOVA, simple linear regression, and correlation. Graduate standing and an undergraduate course in mathematics at the level of college algebra or higher are assumed.
Purpose
One of the things you have probably observed as a professional is that there is still a lot to be learned about your profession. When you read books and articles on teaching, coaching, recreation, or health and fitness, you constantly encounter descriptions of research results. You know, the old "the subjects were divided into four treatment groups and a control group and … these differences are significant (p < .05) …" Ever wonder what all that stuff means? If you think your own professional development readings have a lot of this stuff in them, wait until you get the reading list for a typical KINE 63xx course. Of course, now that you are undertaking graduate study, you will be expected not only to read about the research of others but also to conduct research of your own. MATH 6301 is an introductory statistics course designed with two basic goals:
- to get you started on being able to interpret the statistical results of research in your area
- to get you familiar enough with the basics of statistical analysis so that you know when and how and whether you can apply them in your own research
Objectives
- Module 0 - Introduction
Become familiar with the course syllabus and the course tools/materials - Module 1 - Descriptive Statistics, 1
Given a dataset, construct and interpret appropriate graphical representations of the data, calculate various summary statistics for the data, investigate the data for potential normality - Module 2 - Descriptive Statistics, 2
Given a dataset consisting of paired data, construct and interpret a scatter plot to representing the data.
Use statistical software to generate sampling distributions and discuss the resulting data - Module 3 - Probability Distributions
Given a probability distribution, calculate its expected value and variance.
State and apply the Central Limit Theorem. - Module 4 - Hypothesis Testing
Given a problem description, set up and carry out a hypothesis test for one or two populations, using either the z-test or the t-test, as appropriate, including estimation of the p-value of the result. - Module 5 - One-Way Analysis of Variance
Given a problem description, set up and carry out a hypothesis test for equality of means for several independent samples, using either a partial computer output or the SPSS software. The test will include the application of a post-hoc analysis, if appropriate. - Module 6 - More-Way Analysis of Variance
Given a problem description, set up and carry out a hypothesis test for a two-factor experiment, with independent samples, or a one-factor experiment with blocks, using either a partial computer output or the SPSS software. The test will include the application of a post-hoc analysis, if appropriate. - Module 7 - Regression and Correlation
Given a dataset consisting of paired data, test the hypothesis that the slope of the regression line and/or the correlation coefficient is zero and plot and interpret the mean and individual prediction intervals. Sufficient information for the test may be provided, may come from computation, or may be gotten from SPSS.
Materials
To see a complete list of materials needed for this course, as well as any important notes and instructions provided by the instructor, visit the UTTC Book Lists.
Prerequisites: Graduate standing
Credits:3
Level:Graduate
Faculty
Doug Hale
hale_d@utpb.edu
432-552-2254