##
Partition-primed Probability Judgement Task for Car Dealership

### Description

In this study participants were asked to judge how likely it is that a
customer trades in a coupe or that a customer buys a car form a
specific salesperson out of four possible salespersons.

### Usage

`data(CarTask)`

### Format

A data frame with 155 observations on the following 3 variables.

`task`

a factor with levels `Car`

and
`Salesperson`

indicating the condition.

`probability`

a numeric vector of the estimated probability.

`NFCCscale`

a numeric vector of the NFCC scale.

### Details

All participants in the study were undergraduate students at The
Australian National University, some of whom obtained course credit in
first-year Psychology for their participation in the study.

The NFCC scale is a combined scale of the Need for Closure and Need
for Certainty scales which are strongly correlated.

For `task`

the questions were:

- Car
What is the probability that a customer trades in a coupe?

- Salesperson
What is the probability that a customer buys a
car from Carlos?

### Source

Taken from Smithson et al. (2011) supplements.

### References

Smithson, M., Merkle, E.C., and Verkuilen, J. (2011). Beta
Regression Finite Mixture Models of Polarization and Priming.
*Journal of Educational and Behavioral Statistics*, **36**(6), 804–831.
doi: 10.3102/1076998610396893

Smithson, M., and Segale, C. (2009). Partition Priming in Judgments of
Imprecise Probabilities. *Journal of Statistical Theory and
Practice*, **3**(1), 169–181.

### Examples

```
data("CarTask", package = "betareg")
library("flexmix")
car_betamix <- betamix(probability ~ 1, data = CarTask, k = 3,
extra_components = list(extraComponent(type = "uniform", coef = 1/2,
delta = 0.01), extraComponent(type = "uniform", coef = 1/4, delta = 0.01)),
FLXconcomitant = FLXPmultinom(~ task))
```