# length

## Hierarchical Linear Model

Linear regression probably is the most familiar technique in data analysis, but its application is often hamstrung by model assumptions. For instance, if the data has a hierarchical structure, quite often the assumptions of linear regression are feasible only at local levels. We will investigate an extension of the linear model to bi-level hierarchies.

## Support Vector Machine with GPU, Part II

In our last tutorial on SVM training with GPU, we mentioned a necessary step to pre-scale the data with rpusvm-scale, and to reverse scaling the prediction outcome. This cumbersome procedure is now simplified with the latest RPUSVM.

## Support Vector Machine with GPU

Most elementary statistical inference algorithms assume that the data can be modeled by linear parameters with a normally distributed error component. A new class of algorithms called support vector machine (SVM) remove such constraint.

## Interval Estimate of Population Proportion

A tutorial on computing the interval estimate of population proportion at given confidence level.

## Point Estimate of Population Proportion

A tutorial on computing the point estimate of population proportion from a simple random sample.

## Interval Estimate of Population Mean with Unknown Variance

A tutorial on computing the interval estimate of population mean at given confidence level. The variance of the population is assumed to be unknown.

## Interval Estimate of Population Mean with Known Variance

A tutorial on computing the interval estimate of population mean at given confidence level. The variance of the population is assumed to be known.

## Vector

A tutorial on the concept of vectors in R. Discuss how to create vectors of numeric, logical and character string data types.