Linear Regression on Batı Şengül
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Recent content in Linear Regression on Batı ŞengülHugo -- gohugo.iobatisengul@gmail.combatisengul@gmail.comWed, 20 Jun 2018 00:00:00 +0000Spike and slab: Bayesian linear regression with variable selection
http://www.batisengul.co.uk/post/spike-and-slab-bayesian-linear-regression-with-variable-selection/
Wed, 20 Jun 2018 00:00:00 +0000batisengul@gmail.comhttp://www.batisengul.co.uk/post/spike-and-slab-bayesian-linear-regression-with-variable-selection/Spike and slab is a Bayesian model for simultaneously picking features and doing linear regression. Spike and slab is a shrinkage method, much like ridge and lasso regression, in the sense that it shrinks the “weak” beta values from the regression towards zero. Don’t worry if you have never heard of any of those terms, we will explore all of these using Stan. If you don’t know anything about Bayesian statistics, you can read my introductory post before reading this one.Correlation in linear regression
http://www.batisengul.co.uk/post/correlation-in-linear-regression/
Sun, 03 Sep 2017 00:00:00 +0000batisengul@gmail.comhttp://www.batisengul.co.uk/post/correlation-in-linear-regression/If you have a data set with large number of predictors, you might use some basic models to try and eliminate some of the predictors that don’t show a significant relationship to the response variable. In such cases it is important to look at the correlation between the predictors. How important? Let’s find out.
Let us consider a very simple example here with two predictors and one response variable.
set.seed(2017) data = tibble(x1 = rnorm(1000)) %>% mutate(y = 2 * x1^3 + rnorm(1000), x2 = 1.