Auto Pollution Filter Noise — auto.noise • emmeans

Three-factor experiment comparing pollution-filter noise for two filters, three sizes of cars, and two sides of the car.

Usage

auto.noise

Format

A data frame with 36 observations on the following 4 variables.

noise: Noise level in decibels (but see note) - a numeric vector.
size: The size of the vehicle - an ordered factor with levels S, M, L.
type: Type of anti-pollution filter - a factor with levels Std and Octel
side: The side of the car where measurement was taken – a factor with levels L and R.

Source

The dataset was obtained from the Data and Story Library (DASL) at Carnegie-Mellon University. Apparently it has since been removed. The original dataset was altered by assigning meaningful names to the factors and sorting the observations in random order as if this were the run order of the experiment.

Details

The data are from a statement by Texaco, Inc., to the Air and Water Pollution Subcommittee of the Senate Public Works Committee on June 26, 1973. Mr. John McKinley, President of Texaco, cited an automobile filter developed by Associated Octel Company as effective in reducing pollution. However, questions had been raised about the effects of filters on vehicle performance, fuel consumption, exhaust gas back pressure, and silencing. On the last question, he referred to the data included here as evidence that the silencing properties of the Octel filter were at least equal to those of standard silencers.

Note

While the data source claims that noise is measured in decibels, the values are implausible. I believe that these measurements are actually in tenths of dB (centibels?). Looking at the values in the dataset, note that every measurement ends in 0 or 5, and it is reasonable to believe that measurements are accurate to the nearest half of a decibel.

Examples

# (Based on belief that noise/10 is in decibel units)
noise.lm <- lm(noise/10 ~ size * type * side, data = auto.noise)

# Interaction plot of predictions
emmip(noise.lm, type ~ size | side)


# Confidence intervals
plot(emmeans(noise.lm, ~ size | side*type))