MA 171 Course Description

MA171 INTRODUCTION TO PROBABILITY AND STATISTICS (4 Cr.)

COURSE DESCRIPTION

Satisfies the formal communication studies requirement [Division V]

Prerequisite

MA 103 or MA 104 or MA 111 or satisfactory score on the Math Placement Exam. A laptop is required. With the University requirement of a grade of C- or better.

Offered: Fall, Winter, Summer

General Introduction and Goals

The course consists of a study of the methods of elementary probability and statistics. Some time is devoted to finding probabilities for both discrete and continuous probability functions, and discussing the role probability plays in estimation and decision making. The main emphasis of the course, however, is on methods of describing data, finding sampling estimates and testing hypotheses. Throughout the course, applications are stressed as is the interpretation and understanding of the statistics and methods used.

The student will:

  • become familiar with basic probability and statistical methodology and terminology;
  • learn how to present data graphically and be able to read and interpret such presentations;
  • learn how to calculate estimates and other statistics, and interpret and compare statistics;
  • find probabilities and understand the role of probability in statistical decision making;
  • learn how to test a hypothesis and use statistical procedures to help make decisions; and
  • be able to identify an appropriate statistical procedure to use in a given situation and identify when a procedure is improperly used.

Course Content

  1. Methods for Describing Sets of Data
    • Types of data
    • Graphical methods for describing data
    • Measures of central tendency
    • Measures of variability and relative standing
  2. Probability
    • Events, sample spaces and simple probabilities
    • Compound events and rules for calculating their probabilities
    • Conditional probability
  3. Discrete Random Variables
    • Probability distributions for discrete random variables
    • Expected values
    • Binomial distribution
  4. Continuous Random Variables
    • Continuous probability distributions
    • The normal distribution
    • Approximating a binomial distribution with a normal distribution
  5. Sampling Distributions
    • Sampling distribution
    • The Central Limit Theorem
  6. Estimation and Tests of Hypotheses
    • Point and confidence interval estimates and tests of hypotheses for:
      • A population mean: large and small samples
      • A binomial population proportion
      • A population variance
    • Inferences about:
      • The difference between two means: independent samples
      • The difference between two means: dependent samples
      • The difference between two binomial proportions
  7. Analysis of Variance
  8. The Chi Square Test and Contingency Tables
    • One dimensional count data
    • Contingency tables
  9. Simple Linear Regression
    • Least squares model and assumptions
    • Regression estimates and prediction
    • Estimating and interpreting correlation Inferences about the slope and correlation