You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?
In this webinar, Kim Love shows some descriptive statistics and graphics that you can produce before running a model to help with that decision.
She also discusses additional descriptive statistics and graphics that you should check before interpreting the results of a linear regression model (yes, checking model assumptions).
Techniques included in this training are univariate and bivariate descriptive statistics, histograms, normal QQ plots, and scatterplots, which will be applied to variables in the model as well as residuals and predicted values.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Kim is a workshop instructor for The Analysis Factor and owner/lead consultant at K.R. Love Quantitative Consulting and Collaboration.
She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center.
Kim has more than a decade of professional and academic experience in the fields of regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design. She has a B.A. in mathematics from the University of Virginia, and an M.S. and PhD in statistics from Virginia Tech.
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