# 2165 search results for "regression"

## The Mind Is Flat! So Stop Overfitting Choice Models

May 15, 2014
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Conjoint analysis and choice modeling rely on repeated observations from the same individuals across many different scenarios where the features have been systematically manipulated in order to estimate the impact of varying each feature. We believe th...

## Quick History: glm()

May 15, 2014
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by Joseph Rickert I recently wrote about some R resources that are available for generalized linear models (GLMs). Looking over the material, I was amazed by the amount of effort that is continuing to go into GLMs, both with with respect to new theoretical developments and also in response to practical problems such as the need to deal with...

## Identifying periods of change in time series with GAMs

May 15, 2014
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In previous posts (here and here) I looked at how generalized additive models (GAMs) can be used to model non-linear trends in time series data. In my previous post I extended the modelling approach to deal with seasonal data where we model both the within year (seasonal) and between year (trend) variation with separate smooth functions....

## A comment on “We cannot afford to study effect size in the lab” from the DataColada blog

May 6, 2014
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In a recent post on the DataColada blog, Uri Simonsohn wrote about “We cannot afford to study effect size in the lab“. The central message is: If we want accurate effect size (ES) estimates, we need large sample sizes (he suggests four-digit n’s). As this is hardly possible in the lab we have to use

## Le Monde puzzle [#865]

May 5, 2014
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A Le Monde mathematical puzzle in combinatorics: Given a permutation σ of {1,…,5}, if σ(1)=n, the n first values of σ are inverted. If the process is iterated until σ(1)=1, does this always happen and if so what is the maximal  number of iterations? Solve the same question for the set {1,…,2014}. I ran the following basic R

## Five Reasons to Teach Elementary Statistics With R: #3

May 4, 2014
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Introduction Reason #3: RStudio’s shiny Examples “Slow” Simulation Understanding Model Assumptions Types of Error Illustrating Fine Points Playing Games

## Five Reasons to Teach Elementary Statistics With R: #3

May 4, 2014
By

Introduction Reason #3: RStudio’s shiny Examples “Slow” Simulation Understanding Model Assumptions Types of Error Illustrating Fine Points Playing Games

## Five Reasons to Teach Elementary Statistics With R: #3

May 4, 2014
By

Introduction Reason #3: RStudio’s shiny Examples “Slow” Simulation Understanding Model Assumptions Types of Error Illustrating Fine Points Playing Games

## Five Reasons to Teach Elementary Statistics With R: #3

May 4, 2014
By

Introduction Reason #3: RStudio’s shiny Examples “Slow” Simulation Understanding Model Assumptions Types of Error Illustrating Fine Points Playing Games

## Jeffreys’ Substitution Posterior for the Median: A Nice Trick to Non-parametrically Estimate the Median

May 3, 2014
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While reading up on quantile regression I found a really nice hack described in Bayesian Quantile Regression Methods (Lancaster & Jae Jun, 2010). It is called Jeffreys’ substitution posterior for the median, first described by Harold Jeffreys in his Theory of Probability, and is a non-parametric method for approximating the posterior of the median. What makes it...