This is a sample recipe for a two-factor, multi-level experiment. It uses data retrieved from "Cars.csv" to determine if the model year of a vehicle or the country in which a vehicle was made has any effect on the vehicle's horsepower. An analysis of variance is performed to determine is the variation of horse power means between vehicle samples is a result of the vehicle model year and the country of origin. A Tukey's Honestly Significant Difference is performed to identify specifically which horse power means are significantly different than each other.

The following document details the analysis a two-factor, multi-level experiment of the data set 'storms' from the package 'nasaweather.' An analysis of variance (ANOVA) test is used to determine the statistical relationship between the pressure means of storms that occurred in different months and in different years. An ANOVA test is also performed to determine the statistical relationship between the pressure means as a result of the interaction effect between the month and the year that a storm had occurred. After the analysis, it was determined that the probability that the variance of pressure values can be attributed to the variance in the month, the year, and the month/year interaction was very high.

The following analysis is performed on the data set, 'storms,' from the package 'nasaweather.' The recipe describes a two-factor, multi-level experiment that identifies the relationship between the 'month' and the 'year' in which a tropical stormed occurred and its recorded atmospheric 'pressure' measurements. An analysis of variance (ANOVA) test is performed to determine if the pressure means recorded from different months and years are the same. After the analysis, it can be concluded that the probability that the variance in pressure can be explained by the month, the year, and the month/year interaction is very high.