ex1217: Pollution and Mortality

In Sleuth3: Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

Description

Complete data set for problem introduced in ex1123. Data from early study designed to explore the relationship between air pollution and mortality.

Usage

ex1217

Format

A data frame with 60 observations on the following 17 variables.

CITY

a character vector indicating the city

Mortality

total age-adjusted mortality from all causes

Precip

mean annual precipitation (inches)

Humidity

percent relative humidity (annual average at 1:00pm)

JanTemp

mean January temperature (degrees F)

JulyTemp

mean July temperature (degrees F)

Over65

percentage of the population aged 65 years or over

House

population per household

Educ

median number of school years completed for persons 25 years or older

Sound

percentage of the housing that is sound with all facilities

Density

population density (in persons per square mile of urbanized area)

NonWhite

percentage of population that is nonwhite

WhiteCol

percentage of employment in white collar occupations

Poor

percentage of households with annual income under $3,000 in 1960

HC

relative pollution potential of hydrocarbons

NOX

relative pollution potential of oxides of nitrogen

SO2

relative pollution potential of sulfur dioxide

Source

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

References

McDonald, G.C. and Ayers, J.A. (1978). Some Applications of the “Chernoff Faces”: A Technique for Graphically Representing Multivariate Data in Wang, P.C.C. (ed.) Graphical Representation of Multivariate Data, Academic Press.

See Also

ex1123

Examples

str(ex1217)

Example output

'data.frame':	60 obs. of  17 variables:
 $ CITY     : Factor w/ 60 levels "Akron, OH","Albany, NY",..: 49 56 47 28 34 16 32 29 22 18 ...
 $ Mortality: num  791 824 840 844 858 ...
 $ Precip   : int  13 28 10 43 25 35 60 11 31 15 ...
 $ Humidity : int  71 54 61 54 58 54 60 47 61 38 ...
 $ JanTemp  : int  49 32 55 32 12 46 67 53 24 30 ...
 $ JulyTemp : int  68 81 70 74 73 85 82 68 72 73 ...
 $ Over65   : num  7 7 7.3 10.1 9.2 7.1 10 9.2 9 8.2 ...
 $ House    : num  3.36 3.27 3.11 3.38 3.28 3.22 2.98 2.99 3.37 3.15 ...
 $ Educ     : num  12.2 12.1 12.1 9.5 12.1 11.8 11.5 12.1 10.9 12.2 ...
 $ Sound    : num  90.7 81 88.9 79.2 83.1 79.9 88.6 90.6 82.8 84.2 ...
 $ Density  : int  2702 3665 3033 3214 2095 1441 4657 4700 3226 4824 ...
 $ NonWhite : num  3 7.5 5.9 2.9 2 14.8 13.5 7.8 5.1 4.7 ...
 $ WhiteCol : num  51.9 51.6 51 43.7 51.9 51.2 47.3 48.9 45.2 53.1 ...
 $ Poor     : num  9.7 13.2 14 12 9.8 16.1 22.4 12.3 12.3 12.7 ...
 $ HC       : int  105 4 144 11 20 1 3 648 5 17 ...
 $ NOX      : int  32 2 66 7 11 1 1 319 3 8 ...
 $ SO2      : int  3 1 20 32 26 1 1 130 10 28 ...

Sleuth3 documentation built on May 2, 2019, 6:41 a.m.