# Department of Statistics

## STAT 509

509–Statistics for Engineers. (3) (Prereq: Math 142 or equivalent) Basic probability and statistics with applications and examples in engineering. Elementary probability, random variables and their distributions, random processes, statistical inference, linear regression, correlation and basic design of experiments with application to quality assurance, reliability, and life testing.

Sample Course Homepage: Recent Semester

Usually Offered: Fall, Spring, and Summer I Semesters

Learning Outcomes: By the end of the term successful students should be able to do the following:

• Understand and be able to correctly use basic statistical terminology.
• Recognize and evaluate variation in data using basic parameter estimation and hypothesis testing.
• Compare data sets using parameter estimation, hypothesis testing and analysis of variance.
• Recognize and evaluate relationships between two variables using simple linear regression.
• Apply basic 2K design of experiments in order to study and improve engineering processes.
• Understand and be able to apply simple principles of probability, parameter estimation, hypothesis testing, analysis of variance, simple linear regression, and design of experiments to engineering applications.

Current Textbook: Statistical Methods for Engineers (Latest Edition), by Geoffrey Vining and Scott M. Kowalski, Thomson, Brooks/Cole.

 Topics Covered Chapters Time Basic Probability – counting, basic laws and elementary theorems; independent events. 3.1 1 week Discrete Random Variables and Distributions – binomial, hypergeometric, Poisson, mean and variance, Poisson Process. 3.2 2 weeks Continuous Random Variables and Distributions – uniform, exponential, normal, and probability plots. 3.3-3.4 2 weeks Random Sampling and Sampling Distributions – central limit theorem and t distribution, chi-square, and F distributions. 3.6-3.7 1 week Inference for Proportions – confidence intervals, hypothesis testing, and sample size. 4.1, 4.2, 4.4 2 weeks Inference for a Single Mean – confidence intervals, hypothesis testing, and sample size. 4.3 1 week Inference for Variances – confidence intervals, hypothesis testing, one and two populations. 4.7 1 week Inference for Two Populations – confidence intervals, hypothesis testing, independent and dependent samples. 4.5-4.6 1 week Analysis of Variance. 1 week Simple Linear Regression (as time permits) – curve fitting, inferences about estimated parameters, adequacy of models, linear correlation. 6.1-6.2 1 week Design of Experiments – 2K factorial designs and half fractions. 7.1-7.3 1 week

The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.

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