# Posts Tagged ‘ Linear Models ’

## Maximum likelihood

October 13, 2011
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$Maximum likelihood$

This post is one of those ‘explain to myself how things work’ documents, which are not necessarily completely correct but are close enough to facilitate understanding. Background Let’s assume that we are working with a fairly simple linear model, where … Continue reading →

## Simulating data following a given covariance structure

October 12, 2011
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Every year there is at least a couple of occasions when I have to simulate multivariate data that follow a given covariance matrix. For example, let’s say that we want to create an example of the effect of collinearity when … Continue reading →

## Assumptions of the Linear Model

October 6, 2011
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Linear Assumptions from the Analysis Factor – Assumptions of linear regression (and ANOVA) are about the residuals, not the normality or independence of the response variable (Y). If you don’t know what this means be sure to read this brief … Continue reading →

## Linear regression with correlated data

October 5, 2011
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I started following the debate on differential minimum wage for youth (15-19 year old) and adults in New Zealand. Eric Crampton has written a nice series of blog posts, making the data from Statistics New Zealand available. I will use … Continue reading →

## Model Validation: Interpreting Residual Plots

July 18, 2011
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When conducting any statistical analysis it is important to evaluate how well the model fits the data and that the data meet the assumptions of the model. There are numerous ways to do this and a variety of statistical tests to evaluate deviations from model assumptions. However, there is little general acceptance of any of the statistical tests. Generally...

## Variable selection using automatic methods

May 22, 2010
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When we have a set of data with a small number of variables we can easily use a manual approach to identifying a good set of variables and the form they take in our statistical model. In other situations we may have a large number of potentially important variables and it soon becomes a time

## Linear regression models with robust parameter estimation

May 15, 2010
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There are situations in regression modelling where robust methods could be considered to handle unusual observations that do not follow the general trend of the data set. There are various packages in R that provide robust statistical methods which are summarised on the CRAN Robust Task View. As an example of using robust statistical estimation in

## R Tips in Stat 511

March 22, 2010
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$\reverse C(X'X)^{-1}X'$

Here are some (trivial) R tips in the course Stat 511. I’ll update this post till the semester is over. Formatting R Code Reading code is pain, but the well-formatted code might alleviate the pain a little bit. The function tidy.source() in the animation package can help us format our R code automatically. By default

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