ComenzarEmpieza gratis

A logistic model for beer demand

The linear model does not fit the data when it comes to predicting purchase probabilities. You need a response function that bounds the model predictions between zero and one.

The logistic response function can do this job for you. Therefore, you will need the function glm(). The function glm() works very similar to the lm() function. The main difference is the additional family argument. As HOPPINESS is a binary variable, you have to specify the family argument as binomial.

Este ejercicio forma parte del curso

Building Response Models in R

Ver curso

Instrucciones del ejercicio

  • Explain HOPPINESS by price.ratio using the function glm() and the argument family = binomial. Assign the result to an object named logistic.model.
  • Obtain the coefficients of the logistic.model by using the function coef().

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Explain HOPPINESS by price.ratio
___ <- ___(___, family = ___, data = choice.data)

# Obtain the coefficients
Editar y ejecutar código