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.

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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.

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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 with this issue.

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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 serial correlation.

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