# Imprecise Probabilities for Sunday Weather and Boeing Stock Task

### Description

In this study participants were asked to estimate upper and lower probabilities for event to occur and not to occur.

### Usage

1 |

### Format

A data frame with 242 observations on the following 3 variables.

`task`

a factor with levels

`Boeing stock`

and`Sunday weather`

.`location`

a numeric vector of the average of the lower estimate for the event not to occur and the upper estimate for the event to occur.

`difference`

a numeric vector of the differences of the lower and upper estimate for the event to occur.

### Details

All participants in the study were either first- or second-year undergraduate students in psychology, none of whom had a strong background in probability or were familiar with imprecise probability theories.

For the sunday weather task see `WeatherTask`

. For the Boeing
stock task participants were asked to estimate the probability that
Boeing's stock would rise more than those in a list of 30 companies.

For each task participants were asked to provide lower and upper estimates for the event to occur and not to occur.

### Source

Taken from http://dl.dropbox.com/u/1857674/betareg/betareg.html.

### References

Smithson, M., Merkle, E.C., and Verkuilen, J. (in press). Beta
Regression Finite Mixture Models of Polarization and
Priming. *Journal of Educational and Behavioral Statistics*.

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

### Examples

1 2 3 4 5 6 | ```
data("ImpreciseTask", package = "betareg")
library("flexmix")
wt_betamix <- betamix(location ~ difference * task, data = ImpreciseTask, k = 2,
extra_components = extraComponent(type = "betareg", coef =
list(mean = 0, precision = 8)),
FLXconcomitant = FLXPmultinom(~ task))
``` |