Category: R

Jason Bryer takes us through a classic introductory problem to Bayesian statistics:

I find that when teaching statistics (and probability) it is often helpful to simulate data first in order to get an understanding of the problem. The Monty Hall problem recently came up in a class so I implemented a function to play the game.

The Monty Hall problem results from a game show, Let’s Make a Deal, hosted by Monty Hall. In this game, the player picks one of three doors. Behind one is a car, the other two are goats. After picking a door the player is shown the contents of one of the other two doors, which because the host knows the contents, is a goat. The question to the player: Do you switch your choice?

This is one of the biggest “aha!” moments in statistics, in the sense that it is not intuitively obvious and is easy to get wrong, but once you understand why it is true, it makes reasoning over time and knowledge changes easier. H/T R-Bloggers.

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M. Fatih Tüzen fills in the gaps:

Data preprocessing is a cornerstone of any data analysis or machine learning pipeline. Raw data rarely comes in a form ready for direct analysis — it often requires cleaning, transformation, normalization, and careful handling of anomalies. Among these preprocessing tasks, dealing with missing data stands out as one of the most critical and unavoidable challenges.

Missing values appear in virtually every domain: surveys may have skipped questions, administrative registers might contain incomplete records, and clinical trials can suffer from dropout patients. Ignoring these gaps or handling them naively does not just reduce the amount of usable information; it can also introduce bias, decrease statistical power, and ultimately compromise the validity of conclusions. In other words, missing data is not just an inconvenience — it is a methodological problem that demands rigorous attention.

Quite often, we gloss over what to do with missing data when explaining or working through the data science process, in part because it’s a hard problem. This post digs into the specifics of the matter, taking us through eight separate methods. H/T R-Bloggers.

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Adam Gladstone continues a series on using R in Excel:

We have already seen how to obtain descriptive statistics in Part I and how to use lm() in Part II. In this part (Part III) of the series we will look at using R in Excel to perform forecasting and time series analysis.

In the previous two parts we have seen different ways to handle the output from R function calls, unpacking and massaging the data as required. In this part we are going to focus on setting up and interacting with a number of models in the ‘forecast’ package (fpp2).

Read on for the demo. This is getting into territory that is by no means trivial to do natively in Excel.

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