Prerequisites: Students should have completed the Basic Statistics Course or be able to demonstrate proficiency in basic statistical methods.

Contact Hours: 32 to 48 hours

Textbook: Students will be provided with a copy of presentation notes and Mathews, Design of Experiments with Applications in Minitab.

Course Description: This course begins with a review of graphical presentation methods, measures of location and variation, and the hypothesis tests and confidence intervals necessary to analyze and interpret designed experiments. Students will learn to design, analyze, and interpret experiments involving one and multiple variables using Analysis of Variance (ANOVA) and regression techniques. Specific experiment designs to be covered are: the completely randomized design, the randomized block design, factorial designs, designs for building simple linear and nonlinear models, two-factorial designs, fractional factorial designs, central composite designs, Box-Behnken designs, and Plackett-Burman designs. Also hybrid designs which mix qualitative and quantitative variables and designs for analyzing binary response data will be introduced. Students will use the Minitab statistical software package during class and Minitab and DOE skills will be reinforced with homework assignments. Students will use Minitab macros to analyze data and run simulations of engineering problems. Students will also be required to participate in lab exercises and report the results of their experiments in both oral and written form.

1. Use the normal, Student’s t, and F distributions to interpret ANOVA and regression analyses.
2. Use simple graphs to evaluate the normality and equality of variances assumptions.
3. Analyze experiments with qualitative variables using ANOVA.
4. Analyze experiments with quantitative variables using regression analysis.
5. Interpret and modify Minitab exec type macros.
6. Design, build, and use screening and response surface models.
7. Build and interpret statistical models including simple linear, interaction, and quadratic terms.
8. Simplify models using Occam’s Razor.
9. Determine experimental sample sizes to meet specified detection limits and risk levels.
10. Run an experiment using the 12 step method from the planning stage through final reporting.
11. Design, analyze, and interpret experiments involving qualitative and quantitative variables:

• Completely randomized design
• Randomized block design
• General factorial designs
• Designs for building simple linear and nonlinear models in one variable
• Two-factorial designs
• Fractional factorial designs
• Central composite designs for quadratic modeling
• Box-Behnken designs for quadratic modeling
• Plackett-Burman designs for screening many variables

 Last Revised: 01/26/2002. Counter started on 02/09/02.