MATH 370 Syllabus

Probability and Statistics I

Revised: February, 2015

Course Description

Classical probability models, distributions of discrete and continuous random variables, joint probability distributions, mathematical expectation. Prerequisite: MATH 255. Three semester hours.

Student Learning Objectives

By the end of the semester, students will be able to

  • Model events occurring in nature in mathematical notation for future study.
  • Describe populations and predict events when only samples are available.
  • Interpret how industry uses statistical models in the design and production of goods and services.
  • Develop an appreciation for the use of mathematical concepts such as calculus in the development of statistics.
  • Understand the rather short history of statistics, its recent growth in applications, and current development.

Text

Wackerley, Dennis, Mendenhall, William., and Scheaffer, Richard. Introduction to Mathematical Statistics with Applications, Seventh Edition. Brooks/Cole Cengage, 2008.

Grading Procedure

Grading procedures and factors influencing course grade are left to the discretion of individual instructors, subject to general university policy.

Attendance Policy

Attendance policy is left to the discretion of individual instructors, subject to general university policy.

Course Outline

  • Chapter 1: What Is Statistics? (2 days)
    Introduction. Characterizing a Set of Measurements: Graphical Methods. Characterizing a Set of Measurements: Numerical Methods. How Inferences Are Made. Theory and Reality. Summary.

  • Chapter 2: Probability (7 days)
    Introduction. Probability and Inference. A Review of Set Notation. A Probabilistic Model for an Experiment: The Sample-Point Method. Tools for Counting Sample Points. Conditional Probability and the Independence of Events. Two Laws of Probability. Calculating the Probability of an Event: The Event-Composition Method. The Law of Total Probability and Bayes’ Rule. Numerical Events and Random Variables. Random Sampling. Summary.

  • Chapter 3: Discrete Random Variables and Their Probability Distributions (7 days)
    Basic Definition. The Probability Distribution for a Discrete Random Variable. The Expected Value of a Random Variable or a Function of a Random Variable. The Binomial Probability Distribution. The Geometric Probability Distribution. The Negative Binomial Probability Distribution. The Hypergeometric Probability Distribution. The Poisson Probability Distribution.
    Moment and Moment-Generating Functions. Probability-Generating Functions. Tchebysheff's Theorem. Summary.

  • Chapter 4: Continuous Variables and Their Probability Distributions (7 days)
    Introduction. The Probability Distribution for a Continuous Random Variable. Expected Values for Continuous Random Variables. The Uniform Probability Distribution. The Normal Probability Distribution. The Gamma Probability Distribution. The Beta Probability Distribution. Some General Comments. Other Expected Values. Tchebysheff's Theorem. Expectations of Discontinuous Functions and Mixed Probability Distributions. Summary.

  • Chapter 5: Multivariate Probability Distributions (10 days)
    Introduction. Bivariate and Multivariate Probability Distributions. Marginal and Conditional Probability Distributions. Independent Random Variables. The Expected Value of a Function of Random Variables. Special Theorems. The Covariance of Two Random Variables. The Expected Value and Variance of Linear Function of Random Variables. The Multinomial Probability Distribution. The Bivariate Normal Distribution. Conditional Expectation. Summary.

  • Chapter 6: Functions of Random Variables (5 days)
    Introduction. Finding the Probability Function of a Function of Random Variables. The Method of Distribution Functions. The Method of Transformations. The Method of Moment-Generating Functions. Multivariable Transformations Using Jacobians. Order Statistics. Summary.

  • Chapter 7: Sampling Distributions and the Central Limit Theorem (4 days)
    Introduction. Sampling Distributions Related to the Normal Distribution. The Central Limit Theorem. A Proof of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distribution. Summary.

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