This workshop is designed for people who have very little experience with statistics. We strongly recommend that participants view our Basic Data and Research Skills workshop on our Cornell VOD Page beforehand. We will focus on the basic analyses, procedures and best practices that any researcher should consider when faced with a new dataset. All […]
This workshop will provide a hands-on overview of the Bayesian approach to statistical inference. Topics will include the foundations of Bayesian modeling, choice of prior distribution, Monte Carlo simulation, and practical implementation. Examples will be done using R, using the Stan language (https://mc-stan.org). This will be done using the cmdstanr R package (R Interface to […]
A receiver operating characteristic (ROC) curve is a graph that shows the diagnostic capability of a continuous variable at various thresholds predicting a binary outcome. This graph shows the relationship between the true positive rate to the false positive rate at different threshold values of the predictor variable. Different algorithms for defining the optimal threshold […]
Epidemiology is the study of the distribution and determinants of the presence of disease in populations. The goal of epidemiological studies is often to quantify the relationship between potential risk factors or exposures and disease. To accomplish this goal, there are several commonly used study designs including cross-sectional, cohort, and case-control. The study design dictates […]
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 Python workshop. This is a hands-on workshop and all methods will be demonstrated using the python language and libraries such as pandas and statsmodels.Topics that will be covered include: Chi-squared […]
This workshop will cover the basics of fitting non-linear models. Topics covered include choosing an appropriate non-linear function, choosing appropriate starting values, evaluation model assumptions, evaluating model fits, and prediction. We will also cover ways to compare non-linear fits by group ( i.e. by includingcovariates in a model), as well as touch on covering non-linear […]
This workshop is intended for people who feel comfortable with the material covered in our Introductory Statistics Using R workshop. The dplyr and tidyr packages are versatile tools for efficiently manipulating datasets in the free statistical software package R. These packages employ a constrained number of “verbs” which are functions that correspond to the most […]
Confirmatory factor analysis (CFA) is a form of a structural equation model that allows the estimation of latent variable through reflective indicators. As opposed to exploratory factor analysis, which can be used to estimate an unrestricted measurement model where indicators are allowed to depend on all factors, confirmatory factor analysis is used to estimate a […]
Structural regression models allow researchers to do path analysis on latent variables. At its core, structural regression models have a measurement model, which is estimated using confirmatory factor analysis, and a structural part, which consists of hypotheses about the direct and indirect effects among observed or latent variables. Researchers often conflate the terms structural equation […]
This workshop is designed for people who have very little experience with statistics. We strongly recommend that participants view our Basic Data and Research Skills workshop on our Cornell VOD Page beforehand. We will focus on the basic analyses, procedures and best practices that any researcher should consider when faced with a new dataset. All this will […]