- Aurther Charpentier of Université de Rennes I (aka Freakonometrics) presents a technical post on Maximum Likelihood versus Goodness of Fit, and simulation studies with the Gamma and Lognormal distribution.
- Martin Johnsson wrote a series of five well-written tutorials called
*A slightly different introduction to R,* with tips for beginner R users. Here are the links to parts I, II, III, IV and V.
- Three hunters fire simultaneously at a boar and exactly one bullet hits the animal. Given that they have accuracies 20%, 40% and 60%, what are the probabilities of each hunter hitting the boar?
- Last week, many helpful R articles attracted attention from readers. Two very useful articles that can help advanced R users to reduce their R computation times: Understanding how memory works in R, and Faster for() loops in R, and three on data visualizations: Visualizing neural networks in R, ggplot2: Cheatsheet for Scatterplots and Visualizing Structure in Topic Models.
- Andrew Gelman of Columbia University discusses why statistics is the least important part of data science
- And finally, a book review of
*Thinking, Fast and Slow* by Daniel Kahneman (winner of the 2002 Nobel Prize in Economics), reviewed by Patrick Burns. You might also want to read my own thoughts on the dismal state of behavioral economics.

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Can goodness of fit test statistics be used for association/independence?

There are some goodness of fit test statistics like Pearson chi-square, G square , Freeman-Tukey etc..

Can these test statistics be used for association/independence? ( I mean to see if there is an association between 2 variables)

The chi-square test statistic can be used for independence.

But what about the other test statistics, like G square(log-likelihood ratio), Kolmogorov-Smirnov, Freeman-Tukey, Power divergence test statistic? These are goodness of fit test statistics. But I need to know if they can also be used to determine independence?