ImpreciseTask: Imprecise Probabilities for Sunday Weather and Boeing Stock...

In bayesbr: Beta Regression on a Bayesian Model

Description

In this study, participants had to respond to the greater and lesser probability of the event happening.

Usage

data(ImpreciseTask)

Format

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

task

a variable with responses 0 and 1. If 0 task is Boeing stock, if 1 task is 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 study participants were from the first or second year, none of the participants had an in-depth knowledge of probability.

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.

The task variable that was a qualitative variable was transformed into a quantitative variable to be used by the package functions.#'

References

doi: 10.3102/1076998610396893 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. Journal of Educational and Behavioral Statistics, 36(6), 804–831.

Examples

data("ImpreciseTask", package = "bayesbr")

bbr = bayesbr(location~difference,iter=100,
             data = ImpreciseTask)

bayesbr documentation built on July 17, 2021, 1:07 a.m.