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Penalized Regression for Model Selection: Ridge, LASSO, and Elastic Net

Workshop

Penalized regression is a modern statistical method that has gained much attention over the last decade as researchers in many fields are able to measure far more variables than ever before. Linear regression suffers in two important ways as the number of predictors becomes large. First, overfitting may occur, meaning that the fitted model does […]

Research Integrity and Reproducibility

Workshop

A major principle of the scientific method is replication, or the ability of an experiment or study to be duplicated. A similar idea is “reproducible research,” – that the results of a study can be reproduced given the raw data and the analysis protocols or methods (usually in code, script, or syntax form). With full […]

Introduction to Experimental Design

Workshop

The workshop is intended to introduce participants to the principles of designing experiments and the reasons for adhering to these principles. We will explore methods for controlling extraneous variation in experiments, such as blocking and randomization using specific examples from a variety of fields.

Designing Experiments in JMP

Workshop

The JMP DOE platform provides a user friendly way to construct study designs that accommodate a variety of types of factors and constraints common to experimental research. This workshop will illustrate how to use this platform to design, evaluate and compare: Full factorial designs Randomized block designs Incomplete block designs Split plot designs Response surface […]

Introduction to Sample Size Calculations

Workshop

One of the first steps in planning a new study is determining an appropriate sample size. A sample size should be large enough to have a high probability of detecting an effect of a treatment but small enough that it is within the confines of the study?s budget and minimizes the potential risks to human […]

Simulation-based Sample Size Calculations for Mixed Models

Workshop

Determining appropriate sample sizes for experiments can be a challenge, especially when the anticipated statistical analysis includes mixed models. In this hands-on workshop, we will discuss the goals of sample size and power calculations and how they can be used, as well as ways to simulate sample sizes in R. This method allows for understanding […]

R Packages for RNA-Seq Data

Workshop

This workshop will provide an introduction to statistical methods and software for differential expression analysis based on RNA-seq read counts. All of the methods considered involve a negative binomial distribution for the counts as a starting point. In addition, the statistical methods all utilize some form of empirical Bayes analysis by which gene-specific error variances are ‘shrunk’ towards […]

Intermediate Statistics using Stata

Workshop

This workshop is intended for people who have taken an introductory statistic course and feel comfortable with the material covered in our Introductory Statistics Using Stata workshop. This is a hands-on workshop and all methods will be demonstrated using the statistical software package Stata. Topics that will be covered include: Chi-squared tests One-way and two-way […]

Introduction to Multilevel/Hierarchical/Mixed Models

Workshop

Multilevel models (also referred to as hierarchical models or mixed models) are a class of statistical models that can be used when observations are not independent. Non-independence can occur when data is clustered due to the study design (e.g., data collected on households and their individual members or blocked agricultural studies) or when data is […]

Analysis of Pre-test-Post-test Data

Workshop

A pretest-posttest research design is a simple form of a repeated-measures design where a baseline measurement is taken on subjects randomized to control and intervention groups followed by a post-intervention measurement on the same groups. Generally, the analysis is based on testing three hypotheses concerning (1) the difference in pretest measurements between the control and […]