survey

In our previous statistics tutorials, we have treated population parameters as fixed values, and provided point estimates and confidence intervals for them. An alternative approach is the Bayesian statistics. It treats population parameters as random variables. Probability becomes a measure of our belief in possible outcomes. With new tools like OpenBUGS, tackling new problems requires building new models, instead of creating yet another R command.

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.

A tutorial on performing the Chi-squared goodness of fit test for independent variables.

A tutorial on performing the Chi-squared goodness of fit test for multinomial population.

A tutorial on computing the sampling size for the desired margin of error of population proportion estimate at given confidence level.

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

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

A tutorial on computing the sampling size for the desired margin of error of population mean estimate at given confidence level.

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