An R Introduction to Statistics


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.

Significance Test for Kendall's Tau-b

  A variation of the standard definition of Kendall correlation coefficient is necessary in order to deal with data samples with tied ranks. It known as the Kendall’s tau-b coefficient and is more effective in determining whether two non-parametric data samples with ties are correlated.

Kendall Rank Coefficient

  The correlation coefficient is a measurement of correlation between two random variables. While its computation is straightforward, it is not readily applicable to non-parametric statistics.

Matrix Construction

A discussion on various ways to construct a matrix in R.

Scatter Plot

A tutorial on computing the scatter plot of quantitative data in statistics.

Frequency Distribution of Quantitative Data

A tutorial on computing the frequency distribution of quantitative data in statistics.