An R Introduction to Statistics

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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.

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.