STAT 269 Introductory Statistics (3)

 Catalog Description:

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.)

 Prerequisites:
    1. Some familiarity with quantitative topics and problem solving.
    2. Some experience in using a computer to solve problems.
 Required Course Materials:
 Course Coordinator:
 Course Audience:

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.

 Course Objectives:
    1. To become familiar with both descriptive and inferential analyses.
    2. To use probability as the bridge between descriptive and inferential analysis.
    3. To intuitively understand each concept.
    4. To integrate topics by identifying commonalties.
    5. To understand the limitations of each analysis through consideration of assumptions.
    6. To express general concepts in terms of the application.
    7. To communicate results, clearly and completely, in a manner appropriate to nonquantitative audiences.
    8. 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.
 Topics:
    1. Descriptive Statistics: pictorial and tabular methods; measures of location, variability, and position.
    2. Normal Distributions: percentages and percentiles for normal populations, and Central Limit Theorem.
    3. One-Sample Interval Estimation: properties and assumptions, confidence intervals for a mean and proportion, and sample size determination.
    4. One-Sample Hypothesis Testing: properties and assumptions, tests on a mean and proportion, and relationship to interval estimation.
    5. Two-Sample Interval Estimation: properties and assumptions, confidence intervals for the difference between two means and proportions.
    6. 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.
    7. Linear Correlation: properties and assumptions, descriptive analysis, and hypothesis test.
    8. Analysis of Variance: properties and assumptions, and test for differences among two or more means.
 

Reviewed: September 2011