MATHS 221:  Probability and Statistics (3)

Syllabus

1.   Prerequisite: MATHS 165 or MATHS 162 or permission of the department chairperson.

2.   Course Description:  Elementary probability theory, random variables, discrete and continuous probability distributions.  Theory and applications of descriptive and inferential statistics.  Statistical software and graphing calculator use is integrated throughout the course.

3.   Course Objectives:  The purpose of MATHS 221 is to introduce students to the key concepts of elementary probability theory, and descriptive and inferential statistics.

      Students will:

· Collect, display, analyze, and interpret sample data in a variety of situations using the techniques of exploratory data analysis;

· Understand discrete and continuous probability distributions

· Use experimental and theoretical probabilities as appropriate to formulate and solve problems involving uncertainty;

· Represent relationships between variables with scatterplots, correlation, and regression;

· Explore the probabilistic nature of statistical analyses including hypothesis testing and confidence intervals;

· Demonstrate appropriate use of technological tools for collecting, analyzing, and drawing conclusions from data;

· Recognize the misuse of statistics and common misconceptions of probability.

4.   Course Rationale:  In view of the increasing importance of statistical thinking and reasoning, students in various majors, including all majors in the Department of Mathematical Sciences, are required to gain a background in statistics.  This course is intended to address probability and statistics at a level of sophistication that is comprehensible by students who have successfully studied the concepts of calculus.  Students who enroll in this course include Option 1 mathematics majors, teaching majors in mathematics, computer science majors, and mathematical economics majors. This course emphasizes both the concepts and applications of probability and statistics and addresses students’ computer competency.

5.   Course Content:  The course will cover the following topics:

(a) Descriptive statistics, including measures of central tendency and dispersion, and misuses of descriptive statistics

(b) Elementary probability theory, including common misconceptions of probability

(c) Important distributions of both discrete and continuous types, including the normal, binomial, Poisson, exponential, and uniform distributions, and their means and variances  

(d) Central Limit Theorem

(e) Inferential statistics, including confidence intervals and hypotheses tests

(f) Regression topics, including simple linear regression and correlation analysis

(g) Sampling designs, including simple random sampling

(h) Simulations of probability and statistical experiments

6.   Course Format: The course will be taught through lecture and interactive discussion. Students will work in small groups and will complete computer laboratory assignments that emphasize the analysis of results.

7.   Methods of Evaluating Student Performance: Course grades are determined primarily by student performance on tests and computer assignments, as well as quizzes, homework assignments, and class participation.  Students are evaluated on content material and on their ability to communicate this material to others orally and/or in writing.  The evaluation and weight of these various components are at the discretion of the instructor.

8.   Evaluation of the Course:  The instruction of the course is evaluated by departmental student evaluations and peer evaluations.  The course is reviewed and revised periodically by the Department’s Undergraduate Programs Committee.

[UPC, February 2001; revised by TEAC April 2003; Pierce, October 2005]