Data Analysis

Lynetta Campbell has a passion for finding the stories that are hidden in the numbers and for sharing this love of what statistics reveals with students at all levels.  She holds an M.S. in statistics, an M.S. in Mathematics, an M.S. in Management Science, and a B.S. in Chemical Engineering.

Lynetta worked twelve years in the Chemical Process Industry, during which time she participated in the design, construction, and start-up of projects in polystyrenics.  However, it was her work in the implementation of statistical quality control programs that lead her to focus on data collection and analysis.

In the course of earning her masters degree in statistics, Lynetta gained extensive knowledge in programming with R.  She greatly enjoys working with conventional data analysis tests as well as customized simulations using this language.

Lynetta has assisted graduate students with their data analysis in fields as diverse as engineering, education, and public health.  She can assist clients with their initial exploratory data analysis, usually with a graphical approach to viewing the data. She makes sure each client understands the basics, such as how to properly state the null and alternative hypotheses, and how to test for equivalence using procedures such as independent sample or paired sample t-tests.  She routinely helps clients in the selection of the proper regression methods to employ, helping them to understand generalized linear models, logistic regression, and logit and loglinear models.  Her ultimate goal is always that the client gain full understanding of what his or her data has to say.  In this manner, she has worked with clients whose data was highly qualitative, such as survey data, and she has assisted students with highly quantitative data involving modeling and forecasting.  In addition to working with R, she is highly proficient with SPSS, and JMP, statistical software and programming languages.