One of the biggest challenges that data analysts face is communicating statistical results to our clients, advisors, and colleagues who don’t have a statistics background.
Unfortunately, the way that we learn statistics is not usually the best way to communicate our work to others, and many of us are left on our own to navigate what is arguably the most important part of our work.
In this webinar, we will cover how to: (more…)
Generalized linear mixed models (GLMMs) are incredibly useful tools for working with complex, multi-layered data. But they can be tough to master.
In this follow-up to October’s webinar (“A Gentle Introduction to Generalized Linear Mixed Models – Part 1”), we’ll cover important topics like:
– Distinction between crossed and nested grouping factors
– Software choices for implementation of GLMMs (more…)
The LASSO model (Least Absolute Shrinkage and Selection Operator) is a recent development that allows you to find a good fitting model in the regression context. It avoids many of the problems 
of overfitting that plague other model-building approaches.
In this Statistically Speaking Training, guest instructor Steve Simon, PhD, explains what overfitting is — and why it’s a problem.
Then he illustrates the geometry of the LASSO model in comparison to other regression approaches, ridge regression and stepwise variable selection.
Finally, he shows you how LASSO regression works with a real data set.
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.
Generalized linear mixed models (GLMMs) are incredibly useful—but they’re also a hard nut to crack.
As an extension of generalized linear models, GLMMs include both fixed and random effects. They are particularly useful when an outcome variable and a set of predictor variables are measured repeatedly over time and the outcome variable is a binary, nominal, ordinal or count variable. These models accommodate nesting of subjects in higher level units such as schools, hospitals, etc., and can also incorporate predictor variables collected at these higher levels.
In this webinar, we’ll provide a gentle introduction to GLMMs, discussing issues like: (more…)
In this webinar, you will see what a hazard function is and describe the interpretations of increasing, decreasing, and constant hazard. Then you will examine the log rank test, a simple test closely tied to the Kaplan-Meier curve, and the Cox proportional hazards model.
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.
In this webinar, we’ll discuss when tables and graphs are (and are not) appropriate and how people engage with each of these media.
Then we’ll discuss design principles for good tables and graphs and review examples that meet these principles. Finally, we’ll show that the choice between tables and graphs is not always dichotomous: tables can be incorporated into graphs and vice versa.
Participants will learn how to bring more thoughtfulness to the process of deciding when to use tables and when to use graphs in their work. They will also learn about design principles and examples they can adopt to create better tables and graphs.
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.