This is the course blog for Math 216: Probability and Statistics for Engineering, as taught by Derek Bruff at Vanderbilt University. (You can find out more about me over on my website, Agile Learning.)

On this blog you’ll find the latest news and resources for the course.  The course itself meets MWF from 12:10 to 1:00 in Stevenson Center 1206.  See the following posts for information about various course activities:

• Textbook Information
• Course Activities Roadmap
• Reading Assignments 101
• Clickers 101
• Social Bookmarking 101 (see also Introduction to Diigo, Introduction to Pinterest)
• Problem Sets 101
• Application Projects 101
• Grading Plan

Here’s my version of the course description:

Math 216 is designed to introduce the concepts and techniques of probability and statistics frequently used in engineering applications.  Few processes in manufacturing or other contexts produce consistently reliable results.  Instead, we expect some level of variability in the results of these processes.  Statistics is the science of quantifying this kind of variability, and it allows one to make informed decisions in the face of such variability.  In this course, we will examine the ideas and tools from statistics that enable us to build and verify models of processes and phenomena, design experiments that produce meaningful data, and analyze such data in ways that quantify variability and inform decision-making.  Along the way, we will also investigate some of the principles and applications of probability that form the basis for statistical analysis and learn a few ways that visualization tools can help uncover patterns and stories in data.

And here’s the catalog description:

Discrete and continuous probability functions, cumulative distributions. Normal distribution. Poisson distribution and Poisson process. Conditional probability and Bayes’ formula. Point estimation and interval estimation. Hypothesis testing. Covariance and correlation. Linear regression theory and the principle of least squares. Monte Carlo methods. Intended for students in Electrical Engineering and Computer Engineering. Prerequisite: multivariable calculus. No credit for students who have completed 218. [3] (No AXLE credit)

If you like your course goals in bullet form, see below.

• Students should be able to find patterns in data using basic data visualization tools.
• Students should be able to apply fundamental statistical techniques to solve data analysis problems in engineering applications.
• Students should be able to interpret results of fundamental statistical analyses in engineering applications.
• Students should understand fundamental statistical techniques and their associated concepts in ways that enable them to solve non-routine problems in engineering applications.
• Students should be able to communicate statistical ideas clearly and effectively both verbally and in writing.

Image: “They’re Coming,” Derek Bruff, Flickr (CC)

Header Image: “BUBBLES,” Lauren Manning, Flickr (CC) – One of several representations of the food Ms. Manning ate in 2010.