ROC Curves are incredibly useful in evaluating any model or process that predicts group membership of individuals.
ROC stands for Receiver Operating Characteristic. This strange name goes back to its original use of assessing the accuracy of sonar readings. Any ROC can tell you how well a process or model distinguishes between true and false positives and negatives.
In this webinar, we’ll talk about what ROC Curves do, when they’re useful, and how to interpret the curve and some related statistics.
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
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
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All resampling techniques are based on the idea of repeatedly estimating a statistic based on subsets of the sample.
There are many practical applications, including estimating standard errors when they can’t be based on a theoretical distribution (a.k.a., when distributional assumptions are not met).
In this webinar, we’ll talk about some of the most common resampling techniques, including the jacknife and bootstrap, how they work, and situations in which they’re useful.
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
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
While parametric regression models like linear and logistic regression are still the mainstay of statistical modeling, they are not the only, nor always the best, approach to predicting outcome variables.
Classification and Regression Trees (CART) are a nonparametric approach to using values of predictors to find good predictions of values of a response variable.
On each step, the values of a predictor variable are optimally split such that they predict the values of the response variable. The set of splits across multiple predictors leads to a tree.
CART models work for either categorical or numerical response variables and predictor variables, and they are especially good at revealing complex interactions among predictors. So they work well as either an exploratory technique before or a predictive model instead of logistic or linear regression.
In this webinar, we’ll explore CART modelling and discuss what it is, which options work when, and how to interpret the output.
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
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
Cluster analysis classifies individuals into two or more unknown groups based on a set of numerical variables.
It is related to, but distinct from, a few other multivariate techniques including discriminant Function Analysis, (more…)
What is the relationship between predictors and whether and when an event will occur?
This is what event history (a.k.a., survival) analysis tests.
There are many flavors of Event History Analysis, though, depending on how time is measured, whether events can repeat, etc.
In this webinar, we discussed many of the issues involved in measuring time, including censoring, and introduce one specific type of event history model: the logistic model for discrete time events.
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.
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Complex Surveys use a sampling technique other than a simple random sample. Terms you may have heard in this area include cluster sampling, stratified sampling, oversampling, two-stage sampling, and primary sampling unit.
Complex Samples require statistical methods that take the exact sampling design into account to ensure accurate results.
In this webinar, guest instructor Dr. Trent Buskirk will give you an overview of the common sampling techniques and their effects on data analysis.
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
Trent D. Buskirk, Ph.D. is the Vice President of Statistics and Methodology, Marketing Systems Group.
Dr. Buskirk has more than 15 years of professional and academic experience in the fields of survey research, statistics, as well as SPSS, SAS, and R.
Dr. Buskirk has taught for more than a decade at the University of Nebraska and Saint Louis University where he was an Associate Professor of Biostatistics in the School of Public Health.
Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
