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

### 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.