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.

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.