These times represent times between gondolas at Sterling Winery. The main take-away: there are 7 cars, as evidenced by the somewhat regular increases in splits between every 7 cars. The reason the times are slightly non-constant is that the gondolas come off the tracks, so times will change a little between each period.

## Usage

winery_cars

## Format

A data frame with 52 observations on the following 2 variables.

obs_number

The observation number, e.g. observation 3 was immediately preceded by observation 2.

time_until_next

Time until this gondola car arrived since the last car had left.

## Source

In-person data collection by David Diez (OpenIntro) on 2013-07-04.

## Details

Important context: there was a sufficient line that people were leaving the winery.

So why is this data valuable? It indicates that the winery should add one more car since it has a lot of time wasted every 7th car. By adding another car, fewer visitors are likely to be turned away, resulting in increased revenue.

## Examples


winery_cars$car_number <- rep(1:7, 10)[1:nrow(winery_cars)] col <- COL[ifelse(winery_cars$car_number == 3, 4, 1)]
plot(winery_cars[, c("obs_number", "time_until_next")],
col = col, pch = 19
)

plot(winery_cars$car_number, winery_cars$time_until_next,
col = fadeColor(col, "88"), pch = 19
)