Credits: 3

### Description

Prerequisite: MATH241.
Integrated statistical methodology for the improvement of products and processes in terms of performance, quality and cost. Designed experimentation. Statistical process control. Software application. Laboratory activities.

### Semesters Offered

Fall 2017, Spring 2018, Summer 2018, Fall 2018, Spring 2019, Summer 2019, Fall 2019, Spring 2020, Fall 2020, Spring 2021, Summer 2021, Fall 2021, Spring 2022, Fall 2022, Summer 2022

### Learning Objectives

#### Textbook

Statistics for Engineers and Scientists by Navidi, 4th edition, McGraw-Hill, 2014.

Other supplemental materials:

• Applied Statistics and Probability for Engineers, Montgomery and Runger, Wiley.
• Probability and Statistics for Engineers and Scientists, Walpole et al., Prentice Hall.
• Introduction to Probability and Statistics for Engineers and Scientists, Ross, Elsevier.

#### Class/Laboratory Schedule

• Two 75 minute lectures each week

### Topics Covered

• Week 1:  Course info; use of statistics in engineering; sample mean and variance.
• Week 2:  Event, sample space, probability, trees; counting rules, permutations, combinations, Venn diagrams; addition and multiplication rules; conditional probability; Bayes's rule.
• Week 3:  Random variables, distributions, expected value and variance; functions of random variables; error-propagation; numerical experimentation.
• Week 4:  Functions of many random variables; design-under-uncertainty; normal, binomial, hypergeometric distributions.
• Week 5:  Poisson and exponential distributions, sampling, estimators, the Central-Limit theorem.
• Week 6:  Review and problem solving.
• Week 7:  Confidence intervals; estimation using numerical simulation.
• Week 8:  Confidence intervals (standard error, T-dist, difference of means, paired observations, proportion, variance).
• Week 9:  Concept of hypothesis testing, P-value (single mean, proportion, type I and II errors), hypothesis testing (variance, two means, pairs).
• Week 10:  Hypothesis testing (goodness-of-t, independence, goodness-of-t).
• Week 11:  Review and problem solving.
• Week 12:  Analysis-of-variance (ANOVA).
• Week 13:  Linear Regression, Multiple Linear Regression.
• Week 14:  Design-of-experiments (DOE).
• Week 15: Review and problem solving.

### Learning Outcomes

• an ability to apply knowledge of mathematics, science, and engineering
• an ability to design and conduct experiments, as well as to analyze and interpret data
• an ability to identify, formulate, and solve engineering problems
• an understanding of professional and ethical responsibility
• an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice

#### Textbook

Statistics for Engineers and Scientists by Navidi, 4th edition, McGraw-Hill, 2014.

Other supplemental materials:

• Applied Statistics and Probability for Engineers, Montgomery and Runger, Wiley.
• Probability and Statistics for Engineers and Scientists, Walpole et al., Prentice Hall.
• Introduction to Probability and Statistics for Engineers and Scientists, Ross, Elsevier.