# 2043 search results for "regression"

## Linear regression with random error giving EXACT predefined parameter estimates

January 26, 2016
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$Linear regression with random error giving EXACT predefined parameter estimates$

When simulating linear models based on some defined slope/intercept and added gaussian noise, the parameter estimates vary after least-squares fitting. Here is some code I developed that does a double transform of these models as to obtain a fitted model with EXACT defined parameter estimates a (intercept) and b (slope). It does so by: 1)

## Bayesian regression with STAN Part 2: Beyond normality

January 26, 2016
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In a previous post we saw how to perform bayesian regression in R using STAN for normally distributed data. In this post we will look at how to fit non-normal model in STAN using three example distributions commonly found in empirical data: negative-binomial (overdispersed poisson data), gamma (right-skewed continuous data) and beta-binomial (overdispersed binomial data).

## How to create confounders with regression: a lesson from causal inference

January 25, 2016
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By Ben Ogorek Introduction Regression is a tool that can be used to address causal questions in an observational study, though no one said it would be easy. While this article won't close the vexing gap between correlation and causation, it will offer specific advice when you're after a causal truth - keep an eye out for...

## Bayesian regression with STAN: Part 1 normal regression

January 8, 2016
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This post will introduce you to bayesian regression in R, see the reference list at the end of the post for further information concerning this very broad topic. Bayesian regression Bayesian statistics turn around the Bayes theorem, which in a regression context is the following: \$\$ P(theta|Data) propto P(Data|theta) times P(theta) \$\$ Where (theta) is

## Regression with Splines: Should we care about Non-Significant Components?

January 4, 2016
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Following the course of this morning, I got a very interesting question from a student of mine. The question was about having non-significant components in a splineregression.  Should we consider a model with a small number of knots and all components significant, or one with a (much) larger number of knots, and a lot of knots non-significant? My initial intuition was to...

## Using segmented regression to analyse world record running times

December 30, 2015
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by Andrie de Vries A week ago my high school friend, @XLRunner, sent me a link to the article "How Zach Bitter Ran 100 Miles in Less Than 12 Hours". Zach's effort was rewarded with the American record for the 100 mile event. Zach Bitter holds the American record for the 100 mile This reminded me of some analysis...

## Regression Diagnostic Plots using R and Plotly

December 25, 2015
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Plotly is a platform for making, editing, and sharing customizable and interactive graphs. Embedding Plotly graphs in a R-Markdown document is very easy. Here, we will genarate a R-Markdown document with embedded Plotly charts to visualize regression diagnostic plots similar to the ones generated by using plot() on a fitted lm() object. R-Studio First step

## Prediction Intervals for Poisson Regression

December 20, 2015
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Different from the confidence interval that is to address the uncertainty related to the conditional mean, the prediction interval is to accommodate the additional uncertainty associated with prediction errors. As a result, the prediction interval is always wider than the confidence interval in a regression model. In the context of risk modeling, the prediction interval

December 9, 2015
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## RRegrs: exploring the space of possible regression models

November 22, 2015
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Machine learning is a field of science that focusses on mathematically describing patterns in data. Chemometrics does this for chemical data. Examples are (nano)QSAR where structural information is related to biological activity. I studied during my Ph...