# 2378 search results for "regression"

## First steps with Non-Linear Regression in R

February 25, 2016
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Drawing a line through a cloud of point (ie doing a linear regression) is the most basic analysis one may do. It is sometime fitting well to the data, but in some (many) situations, the relationships between variables are not linear. In this case one may follow three different ways: (i) try to linearize the

## Multiple regression lines in ggpairs

February 16, 2016
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Abstract Plots including multiple regression lines are added to a matrix of plots generated with the GGally package in R.1 Background Built upon ggplot2, GGally provides templates for combining plots into a matrix through the ggpairs function. Such...

## Automated parameter selection for LOESS regression

February 11, 2016
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Typically, when we want to understand the relationship between two variables we simply regress one on the other, plot the points and fit a linear trend line. To illustrate this, we use the EuStockMarkets datset pre-loaded in R. The data-set contains th...

## 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...

## Mean and Quantile Regression using Mosek

January 19, 2016
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Many of the problems we encounter in Econometrics can be formulated as a linear or a quadratic problem. In this post, I want to approach two traditional problems: Quantile Regression and Ordinary Least Squares as convex problems and how to implement them in R using the package RMosek.

## 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...