16 Jun 2017
In this new time series tutorial, you will learn general tools that are useful for many different forecasting situations. It will describe some methods for benchmark forecasting, methods for checking whether a forecasting model has adequately utilized the available information, and methods for measuring forecast accuracy. These are important tools to have in your forecasting toolbox as each are leveraged repeatedly as you develop and explore a range of forecasting methods.

02 Jun 2017
Time series forecasting is performed in nearly every organization that works with quantifiable data. Retail stores forecast sales. Energy companies forecast reserves, production, demand, and prices. Educational institutions forecast enrollment. Goverments forecast tax receipts and spending. International financial organizations forecast inflation and economic activity. The list is long but the point is short - forecasting is a fundamental analytic process in every organization. This new tutorial gets you started doing some fundamental time series exploration and visualization.

21 Apr 2017
It is often the case that some or many of the variables used in a multiple regression model are in fact *not* associated with the response variable. Including such irrelevant variables leads to unnecessary complexity in the resulting model. Unfortunately, manually filtering through and comparing regression models can be tedious. Luckily, several approaches exist for automatically performing feature selection or variable selection — that is, for identifying those variables that result in superior regression results. This latest tutorial covers a traditional approach known as *linear model selection*.

07 Apr 2017
Although R provides built-in plotting functions, `ggplot2`

has become the preeminent visualization package in R. `ggplot2`

implements the Grammar of Graphics theory making it particularly effective for constructing visual representations of data and learning this library will allow you to make nearly any kind of (static) data visualization, customized to your exact specifications. This intro tutorial will get you started in making effective visualizations with R.

17 Mar 2017
See the new tutorial on resampling methods, which are an indispensable tool in modern statistics. They involve repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model. For example, in order to estimate the variability of a linear regression fit, we can repeatedly draw different samples from the training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fits differ. Such an approach may allow us to obtain information that would not be available from fitting the model only once using the original training sample.