Now that I’m on my winter break, I’ve been taking a little bit of time to read up on some modelling techniques that I’ve never used before. Two such techniques are Random Forests and Conditional Trees. Since both can be used … Continue reading →

Despite the increasing popularity of multilevel regression models, the development of diagnostic tools lagged behind. Typically, in the social sciences multilevel regression models are used to account for the nesting structure of the data, such as students in classes, migrants ...

In the practice of risk modeling, it is sometimes mandatory to maintain a monotonic relationship between the response and each predictor. Below is a demonstration showing how to develop a generalized boosted regression with a monotonic marginal effect for each predictor. Plot of Variable Importance Plot of Monotonic Marginal Effects

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The application of multilevel regression models has become common practice in the field of social sciences. Multilevel regression models take into account that observations on individual respondents are nested within higher-level groups such as schools, classrooms, states, and countries. In ...

Logistic Regression In my first blog post, I have explained about the what is regression? And how linear regression model is generated in R? In this post, I will explain what is logistic regression? And how the logistic regression model is generated in R? Let’s first understand logistic regression. Logistic regression is one of the

In business "Correlation" is generically used as a mutual relationship or connection between two or more things; statistically speaking correlation is the interdependence of variable quantities. I overhear many end users request information on the correlation of variables for prediction use, what they are referring to is actually simple linear regression. I don't mean to outline all

In practice, we often find that count data is not well modeled by Poisson regression, though Poisson models are often presented as the natural approach for such data. In contrast, the negative binomial regression model is much more flexible and is therefore likely to fit better, if the data are not Poisson. In example 8.30 we...