Outliers in data can distort predictions and affect the accuracy, if you don’t detect and handle them appropriately especially in regression models. Why outliers treatment is important? Because, it can drastically bias/change the fit estimates and predictions. Let me illustrate this using the cars dataset. To better understand ... [Read more...]

Before we build stats/machine learning models, it is a good practice to understand which predictors are significant and have an impact on the response variable. In this post we deal with a particular case when both your response and predictor are categorical variables. By the end of this you’... [Read more...]

[Read more...]

[Read more...]

[Read more...]

The for-loop in R, can be very slow in its raw un-optimised form, especially when dealing with larger data sets. There are a number of ways you can make your logics run fast, but you will be really surprised how fast you can actually go. This posts shows a number ... [Read more...]

[Read more...]

[Read more...]

[Read more...]

One of the important assumptions of linear regression is that, there should be no heteroscedasticity of residuals. In simpler terms, this means that the variance of residuals should not increase with fitted values of response variable. In this post, I am going to explain why it is important to check ... [Read more...]