Guest Post: Pruning Your Hypothesis Testing Decision Tree
Joel Smith is the Director of Rapid Continuous Improvement at Keurig Dr. Pepper as well as the co-author of the Applied Statistics Manual. He will be hosting a panel on Leading Successful Data Analysis at the 2019 Minitab Insights Conference.
How to Choose the Best Regression Model
Choosing the correct linear regression model can be difficult. Trying to model it with only a sample doesn’t make it any easier. In this post, I'll review some common statistical methods for selecting models, complications you may face, and provide some practical advice for choosing the best regression model.
7 Top Talks from the Minitab Insights Conference
See what goes into a great Minitab Insights Conference presentation. Great stories others want to hear. Discovering new tools in Minitab software. Engaging walkthroughs of finding insights in your data, and recommendations on how to act on them. All packed into a few days of learning and fun.
Predicting World Cup 2018 with Ordinal Logistic Regression
According to a recent BBC article, England has 4% chance to win the World Cup 2018. I make some predictions using Minitab after gathering data from past World Cup winners. Will this all make a difference? Let’s find out!
Odds Ratios and St. Patrick's Day: Are 4-Leaf Clovers Really All That Lucky?
Learn about odds ratios and logistic regression in Minitab Statistical Software. Investigate relationships and how predictors affect probabilities of responses.
Minitab Statistical Software's Nonlinear Regression Tool
Learn about using nonlinear regression in Minitab to describe complicated relationships between a response variable and one or more predictor variables.
Fighting Wildfires with Statistical Analysis
Fighting Wildfires with Statistical Analysis
How to Avoid Overfitting Your Regression Model
How to Avoid Overfitting Your Regression Model
The Easiest Way to Do Multiple Regression Analysis
The Easiest Way to Do Multiple Regression Analysis
How to Estimate the Probability of a No-Show using Binary Logistic Regression
How to Estimate the Probability of a No-Show using Binary Logistic Regression