water: California water

In alr4: Data to Accompany Applied Linear Regression 4th Edition

Description

Can Southern California's water supply in future years be predicted from past data? One factor affecting water availability is stream runoff. If runoff could be predicted, engineers, planners and policy makers could do their jobs more efficiently. Multiple linear regression models have been used in this regard. This dataset contains 43 years worth of precipitation measurements taken at six sites in the Owens Valley ( labeled APMAM, APSAB, APSLAKE, OPBPC, OPRC, and OPSLAKE), and stream runoff volume at a site near Bishop, California.

Format

This data frame contains the following columns:

Year

collection year

APMAM

Snowfall in inches measurement site

APSAB

Snowfall in inches measurement site

APSLAKE

Snowfall in inches measurement site

OPBPC

Snowfall in inches measurement site

OPRC

Snowfall in inches measurement site

OPSLAKE

Snowfall in inches measurement site

BSAAM

Stream runoff near Bishop, CA, in acre-feet

Source

Source: http://www.stat.ucla.edu.

References

Weisberg, S. (2014). Applied Linear Regression, 4th edition. Hoboken NJ: Wiley.

Examples

head(water)

Example output

Loading required package: car
Loading required package: effects
Loading required package: carData

Attaching package: 'carData'

The following objects are masked from 'package:car':

    Guyer, UN, Vocab

lattice theme set by effectsTheme()
See ?effectsTheme for details.
  Year APMAM APSAB APSLAKE OPBPC  OPRC OPSLAKE  BSAAM
1 1948  9.13  3.58    3.91  4.10  7.43    6.47  54235
2 1949  5.28  4.82    5.20  7.55 11.11   10.26  67567
3 1950  4.20  3.77    3.67  9.52 12.20   11.35  66161
4 1951  4.60  4.46    3.93 11.14 15.15   11.13  68094
5 1952  7.15  4.99    4.88 16.34 20.05   22.81 107080
6 1953  9.70  5.65    4.91  8.88  8.15    7.41  67594

alr4 documentation built on May 2, 2019, 6:40 p.m.