# 1539 search results for "regression"

## Use of freqparcoord for Regression Diagnostics

April 14, 2014
By

This is the third in my series of three posts on my package freqparcoord with Yingkang Xie. (My next post after this will show how to use R to explore one of my favorite examples of “what can go wrong” in statistics.) Here is a very brief review of my previous posts regarding freqparcoord. A

## Regressions with Multiple Fixed Effects – Comparing Stata and R

April 5, 2014
By

In my paper on the impact of the recent fracking boom on local economic outcomes, I am estimating models with multiple fixed effects. These fixed effects are useful, because they take out, e.g. industry specific heterogeneity at the county level - or state specific time shocks. The models can take the form:    where is

## MoneyPuck – Best subsets regression of NHL teams

March 17, 2014
By

Spring is at hand and it is a time of renewal, March Madness and to settle scores in the NHL.  There are many scores to be settled: Flyers vs. Penguins, Blackhawks vs. Red Wings, Leafs vs. Habs and pretty much everyone else vs. the Bruins.  L...

## Regression with multiple predictors

February 18, 2014
By

(This article was first published on Digithead's Lab Notebook, and kindly contributed to R-bloggers) Now that I'm ridiculously behind in the Stanford Online Statistical Learning class, I thought it would be fun to try to reproduce the figure on page 36 of the slides from chapter 3 or page 81 of the book. The result is a curvaceous surface...

## Solutions for Multicollinearity in Regression(2)

February 16, 2014
By

Continue to discuss this topic about multicollinearity in regression. Firstly, it is necessary introduce how to calculate the VIF and condition number via software such as R. Of course it is really easy for us. The vif() in car and kappa() can be applied to calculate the VIF and condition number, respectively. Consider the data from … Continue reading...

## Better living through zero-one inflated beta regression

February 6, 2014
By

Dealing with proportion data on the interval  is tricky. I realized this while trying to explain variation in vegetation cover. Unfortunately this is a true proportion, and can’t be made into a binary response. Further, true 0’s and 1’s rule out beta regression. You could arcsine square root transform the data (but shouldn’t; Warton and Hui 2011)....

## Compare Regression Results to a Specific Factor Level in R

February 6, 2014
By

Including a series of dummy variables in a regression in R is very simple. For example,ols <- lm(weight ~ Time + Diet, data = ChickWeight)summary(ols) The above regression automatically includes a dummy variable for all but the first level of the factor of the Diet variable.Call:lm(formula = weight ~ Time...

## Solutions for Multicollinearity in Regression(1)

February 3, 2014
By

In multiple regression analysis, multicollinearity is a common phenomenon, in which two or more predictor variables are highly correlated. If there is an exact linear relationship (perfect multicollinearity) among the independent variables, the rank of X is less than k+1(assume the number of predictor variables is k), and the matrix will not be invertible. So the strong correlations … Continue reading...

## Princeton’s guide to linear modeling and logistic regression with R

January 31, 2014
By

If you're new to the R language but keen to get started with linear modeling or logistic regression in the language, take a look at this "Introduction to R" PDF, by Princeton's Germán Rodríguez. (There's also a browsable HTML version.) In a crisp 35 pages it begins by taking you through the basics of R: simple objects, importing data,...

## Spurious Regression of Time Series

December 30, 2013
By