- 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.
This entry was posted on Monday, November 18th, 2013 at 2:00 pm and is filed under Uncategorized.
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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?