IowaHawk has a excellent article attempting to reproduce the infamous CRU climate graph using OpenOffice: Fables of the Reconstruction. We thought we would show how to produced similarly bad results using R. If the re-constructed technique is close to what was originally done then so many bad moves were taken that you can’t learn much Related posts:
Suppose we want to create a polynomial that can approximate better the following dataset on the population of a certain Italian city over 10 years. The table summarizes the data:$$\begin{tabular}{|1|1|}\hline Year & Population\\ \hline 1959&4835\\ 1960...
In this post we will see briefly how to implement a logistic regression model if you have categorical variables, or qualitative, organized in double entry contingency tables. In this model the dependent variable (Y) and independent variable (X) are bot...
We use the regression analysis when, from the data sample, we want to derive a statistical model that predicts the values of a variable (Y, dependent) from the values of another variable (X, independent). The linear regression, which is the simplest an...
Most people using LaTex feel that creating tables is no fun. Some days ago I stumbled across a neat function written by Paul Johnson that produces LaTex code as well as LaTex code that can be used within Lyx. The output can be used for regression models and looks like output from the Stata outreg 
I’m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, a...
Although the graphic at the left might not seem a 100% appropriate, it gives a hint to what I am about to do. I want to calculate all possible linear regression models with one dependent and several independent variables. I do not want to address bias and fitting issues or the question if this 
When it comes to assessing application scalability, controlled measurements of the type that can be obtained with tools like Grinder or LoadRunner, are very useful because they provide a direct measurement of the throughput, X(N), as a function of the...
Quantile regression is a statistical technique intended to estimate, and conduct inference about, conditional quantile functions. Just as classical linear regression methods based on minimizing sums of squared residuals enable one to estimate models f...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
