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A probit model for beer demand

You heard other data scientists prefer using the probit response function for modeling purchase decisions. The probit treats purchases decisions as latent propensities. That sounds fancy and makes you nervous, so you try out the probit, too.

You can again use the function glm() to describe the relation HOPPINESS ~ price.ratio. You only need to augment the family argument by binomial(link = probit). As usual, the estimated coefficients are obtained by using the function coef().

Questo esercizio fa parte del corso

Building Response Models in R

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Istruzioni dell'esercizio

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

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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

# Obtain the coefficients
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