Instructor: Paul J. Smith, Statistics Program

Office hours:  MTWTF 11:00-12:00, MTH 1107
Telephone:  (301) 405-5061
E-mail: pjs@math.umd.edu

Tutoring schedule: MTWTF 12:30-2:20, MTH 0306.

Textbook:  Devore, J. L. (2004). Probability and Statistics for Engineering and the Sciences  (7th edition). Brooks/Cole.

Prerequisite:  MATH 141: Calculus II.

Course Description:

STAT 400 is the first semester of a calculus-based introductory course in probability and statistics. Probability is the mathematical treatment of random phenomena, and statistics is the science of collecting, analyzing and interpreting data subject to random variation.  The course emphasizes applicable mathematics rather than abstract theory, and concepts will be illustrated using real-world examples wherever possible.

This course is not like the mathematics courses that you have taken in the past. Probability and statistics require a novel style of thinking and there will be a continual flow of new concepts and ideas.  It is essential to stay current in the course and to work as many exercises as possible to master the material.

Because there are so many new ideas and concepts in STAT 400, it will not be possible to review techniques from algebra or calculus that are used extensively in the course.  You must be proficient in algebra and calculus.  Click here for a list of essential topics.

• Data summary and visualization:  Graphical presentation of data, numerical data summaries of location and spread.

• Probability: Sample space, events, probability axioms; relative frequency interpretation; equally likely outcomes; conditional probability and independence.

• Discrete Random Variables: definitions; probability mass function and cumulative distribution function; expected values and moments; binomial, hypergeometric, geometric, Poisson distributions.

• Continuous Random Variables: densities: probability as an integral; cumulative distribution, expectation, moments, quantiles; uniform, exponential, normal distributions; applications.

• Joint distributions and random sampling: bivariate random variables, joint and marginal distributions; expectation of functions of several random variables, correlation, covariance; independent random variables; mean and variance of sums of independent random variables, laws of expectation; Law of Large Numbers, Central Limit Theorem; distribution of a sample average.

• Statistical inference: populations, statistics, parameters and sampling distributions; point estimators; criteria for accuracy; estimates of means, variances and proportions; confidence intervals for means and proportions, using confidence intervals to test hypotheses.

• Midterms: There will be two midterms, on Friday, June 12 and on Friday, June 26.  Each midterm will count about 20% toward the final course grade.
• Final exam: The final is scheduled for Friday, July 10, from 9:30 until 10:50. It will be comprehensive and will count about 35% toward the course grade.
• Homework: You should be able to solve all problems in the textbook which deal with material covered in the course. Although some problems will be assigned as homework, collected and graded, you should not assume that solving only assigned problems will prepare you for tests. Homework will count about 10% toward the course grade and will not be accepted late.  Click here for homework assignments.
• Quizzes: Everyone loves surprises! Quizzes will count about 15% toward the course grade. The lowest two quiz grades will be dropped from your quiz average. No makeup quizzes will be given.