We often simulate data in SAS or R to confirm analytical results. For example, consider the following problem from the excellent text by Rice:Let U1, U2, and U3 be independent random variables uniform on . What is the probability that the roots...

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Simple root finding and one dimensional integrals algorithms were implemented in previous posts.These algorithms can be used to estimate the cumulative probabilities and quantiles.Here, take normal distribution as an example.Read More: 281 Words Totally

Simple root finding and one dimensional integrals algorithms were implemented in previous posts.These algorithms can be used to estimate the cumulative probabilities and quantiles.Here, take normal distribution as an example.Read More: 281 Words Totally

SOME EMPIRICAL BASES FOR CHOOSING CERTAIN RISK REPRESENTATIONS OVER OTHERS This week DSN posts some thoughts (largely inspired by the work of former colleagues Stephanie Kurzenhäuser, Ralph Hertwig, Ulrich Hoffrage, and Gerd Gigerenzer) about communicating risks to the general public, providing references and delicious downloads where possible. Representations to use less often Single-event probabilities as

Toss one hundred different balls into your basket. Shuffle them up and select one with equal probability amongst the balls. That ball you just selected, it’s special. Before you put it back, increase its weight by 1/100th. Then put it back, mix up the balls and pick again. If you do this enough, at some