ex1221: Predicting Desert Wildflower Blooms

ex1221R Documentation

Predicting Desert Wildflower Blooms

Description

These data are monthly rainfalls from September to March and the subjectively rated quality of the following spring wildflower display for each of a number of years at each of four desert locations in the southwestern United States (Upland Sonoran Desert near Tucson, the lower Colorado River Valley section of the Sonoran Desert, the Baja California region of the Sonoran Desert, and the Mojave Desert). The quality of the display was judged subjectively with ordered rating categories of poor, fair, good, great, and spectacular. The variable Score is numerical variable corresponding to these ordered categories. A goal is to find an equation for predicting quality of wildflower blooms from the rainfall variables.

Usage

ex1221

Format

A data frame with 122 observations on the following 12 variables.

Year

year of observed wildflower season

Region

a factor variable with 4 levels: "baja", "colorado", "mojave", and "upland"

Sep

the September rainfall, in inches

Oct

the October rainfall, in inches

Nov

the November rainfall, in inches

Dec

the December rainfall, in inches

Jan

the January rainfall, in inches

Feb

the February rainfall, in inches

Mar

the March rainfall, in inches

Total

the total rainfall from September through March, in inches

Rating

a factor with a subjective assessment of the quality of wildflower bloom with levels "FAIR", "GOOD", "GREAT", "POOR", and "SPECTACULAR"

Score

a numerical variable corresponding to the order of rating categories, with Poor=0, Fair=1, Good=2, Great=3, and Spectacular=4

Source

Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.

References

Arizona-Sonora Desert Museum, “Wildflower Flourishes and Flops: a 50–Year History,” https://www.desertmuseum.org/programs/flw_wildflwrbloom.php (July 25, 2011).

Examples

str(ex1221)

Sleuth3 documentation built on May 29, 2024, 2:56 a.m.