Analysis of Covariance (ANCOVA) is a type of linear model that combines the best abilities of linear regression with the best of Analysis of Variance.
It allows you to test differences in group means and interactions, just like ANOVA, while covarying out the effect of a continuous covariate.
Through examples and graphs, we’ll talk about what it really means to covary out the effect of a continuous variable and how to interpret results.
Primary to the discussion will be when ANCOVA is and is not appropriate and how correlations and interactions between the covariate and the independent variables affect interpretation.
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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Time Series are economic or other data that are collected over an extended period of time. Many clever methods have been developed to analyze time series, both to understand the factors that 
cause variation and to forecast future values.
In this session, David will introduce us to time series analysis and explain some of the basic techniques for modelling time series and creating forecasts.
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
David Lillis is an applied statistician in Wellington, New Zealand.
His company, Sigma Statistics and Research Limited, provides online instruction, face-to-face workshops on R, and coding services in R.
David holds a doctorate in applied statistics and is a frequent contributor to The Analysis Factor, including our blog series R is Not So Hard.
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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.
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.
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.
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.
Why does ANOVA give main effects in the presence of interactions, but Regression gives marginal effects?
What are the advantages and disadvantages of dummy coding and effect coding? When does it make sense to use one or the other?
How does each one work, really?
In this webinar, we’re going to go step-by-step through a few examples of how dummy and effect coding each tell you different information about the effects of categorical variables, and therefore which one you want in each situation.
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.
