The premise of Bayesian statistics is that distributions are based on personal belief about the shape of such a distribution, rather than the classical assumption which does not take such subjectivity into account. In this regard, Bayesian statistics defines distributions […]

Continue reading »# Category: Regression Analysis

# Multilevel Modelling in R: Analysing Vendor Data

One of the main limitations of regression analysis is when one needs to examine changes in data across several categories. This problem can be resolved by using a multilevel model, i.e. one that varies at more than one level and […]

Continue reading »# Huber vs. Ridge Regressions: Accounting for Outliers

In a previous tutorial, we saw how we can use Huber and Bisquare weightings to adjust for outliers in a dataset. These weightings allow us to adjust our regression analysis to give less weight to extreme values.

Continue reading »# Working with panel data in R: Fixed vs. Random Effects (plm)

Panel data, along with cross-sectional and time series data, are the main data types that we encounter when working with regression analysis.

Continue reading »# Robust Regressions: Dealing with Outliers

It is often the case that a dataset contains significant outliers – or observations that are significantly out of range from the majority of other observations in our dataset. Let us see how we can use robust regressions to deal […]

Continue reading »# OLS and Logistic Regression Models in R

We use linear models primarily to analyse cross-sectional data; i.e. data collected at one specific point in time across several observations. We can also use such models with time series data, but need to be cautious of issues such as […]

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