I E 311. Engineering Data Analysis

1. Course number and name

I E 311. Engineering Data Analysis 

2. Credits and contact hours

3 credit hours = 45 contact hours per semester

3. Instructor’s or course coordinator’s name

 Dr. John Mullen 

4. Text book, title, author, and year

Probability and Statistics for Engineering and the Sciences by Jay L. Devore, 8th edition. Boston, MA: Brooks/Cole 2012. ISBN 0-538-73352-7

a. other supplemental materials

Student Solutions Manual for Devore’s Probability and Statistics for Engineering and the Sciences, 9th ed by Jay L. Devore. Cengage Learning, 2014. ISBN 978-1-305-25180-9

5. Specific course information

a. catalog description: Methodology and techniques associated with identifying and analyzing industrial data.

b. prerequisites: none co-requisites: C- or better in MATH 192

c. required, elective, or selected elective (as per Table 5-1): required

6. Specific goals for the course (a):

  • Provide students with a foundation in descriptive statistics, probability theory, discrete and continuous distributions, and inferential statistics up through confidence intervals and hypothesis testing based on samples from one and then two populations.
  • Develop understanding of why probability and statistics are key components of industrial engineering.
  • Develop critical thinking, assessment, and problem solving skills of students.
  • Develop competency in the theory and application of probability and statistics, which would prepare students to learn multi-sample hypothesis testing, design and analysis of experiments, regression, and other advanced topics in later courses.

b. Criterion 3 Student Outcomes specifically addressed by this course are found in a mapping of outcomes against all CHME courses in the curriculum.

7. Brief list of topics to be covered

  • Descriptive statistics
  • probability
  • discrete random variables (general, binomial, hypergeometric, negative binomial and Poisson) and probability distributions
  • continuous random variables (general, normal, exponential) and probability distributions
  • joint probability distributions and random samples
  • functions of single and jointly-distributed random variables
  • point estimation
  • statistical intervals based on a single sample
  • tests of hypotheses based on a single sample
  • inferences based on two samples

Common Syllabus Addendum

The NMSU Department of Chemical Engineering maintains a syllabus addendum containing course requirements common to all courses with the CHME prefix online.  This document is accessible from the URL: http://chme.nmsu.edu/academics/syllabi/chme-common-syllabus-addendum/