# 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

#### 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