## ENME 392 - Statistical Methods for Product and Process Development

3 Credits

#### 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.

MATH 241

#### Description

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.

#### Goals

This course covers the fundamental aspects of probability and statistics. The overall objective is for students to gain an appreciation of the inherent uncertainty and errors in all engineering and scientific data, and to provide the basic tools from probability and statistics to quantify these uncertainties.

#### Topics

• 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

#### Class/Laboratory Schedule

• Two 75 minute lectures each week

Last Updated By
Johan Larsson, June 2017