Un modello probit per la domanda di birra

Hai sentito che altri data scientist preferiscono usare la funzione di risposta probit per modellare le decisioni di acquisto. Il probit tratta le decisioni di acquisto come propensioni latenti. Suona sofisticato e ti mette un po’ in soggezione, quindi provi anche tu il probit.

Puoi di nuovo usare la funzione glm() per descrivere la relazione HOPPINESS ~ price.ratio. Devi solo aggiungere all’argomento family l’opzione binomial(link = probit). Come al solito, i coefficienti stimati si ottengono con la funzione coef().

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Creare modelli di risposta in R

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Istruzioni dell'esercizio

Spiega HOPPINESS con price.ratio usando la funzione glm() e l’argomento family = binomial(link = probit). Assegna il risultato a un oggetto chiamato probit.model.
Ottieni i coefficienti di probit.model usando la funzione 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

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Creare modelli di risposta in R

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The first chapter introduces you to the basic principles and concepts of market response models. Here, you will learn how to build simple response models for product sales. In addition, you will learn about the theoretical and practical differences between linear and non-linear models for sales responses.

Exercise 1: Fundamentals of market response models Exercise 2: Retail sales Exercise 3: Understanding sales Exercise 4: Linear response models Exercise 5: A linear response model for sales Exercise 6: Making predictions Exercise 7: Predictive performance Exercise 8: Nonlinear response models Exercise 9: Linearizing nonlinear functions Exercise 10: What 's the value added?

An effective marketing strategy combines all the tools available to communicate the benefits of a product. The key is crafting the right mix of these tools to achieve sales increases and market share goals. In the second chapter, you will learn how to incorporate the effects of advertising and promotion in your sales-response model and how to identify the marketing strategy that is most likely to succeed.

Exercise 1: Model extension part 1: Dummy variables Exercise 2: Understanding dummy variables Exercise 3: The effect of display on sales Exercise 4: The effect of multiple dummies on sales Exercise 5: What about price?Exercise 6: Model extensions part 2: Dynamic variables Exercise 7: How to lag?Exercise 8: Adding lagged price effects Exercise 9: More lags Exercise 10: What's the value added?Exercise 11: How many extensions are needed?Exercise 12: Summarizing the model Exercise 13: Unnecessary predictors Exercise 14: Dropping predictors Exercise 15: Eliminating predictors

A company can only be successful in the market if its products have a competitive advantage over those of its rivals. To develop an effective marketing strategy in a competitive environment, it is essential to understand the interrelationship between marketing activity and customer behavior. In this chapter, you will learn how to explain the effects of temporary price changes on customer brand choice by employing logistic and probit response models.

Exercise 1: Modelli per la domanda individuale Exercise 2: Acquisti dei clienti Exercise 3: Riepilogare i dati Exercise 4: Concorrenza Exercise 5: Un modello di probabilità lineare per la domanda di birra Exercise 6: Modelli di risposta logistica Exercise 7: Un modello logistico per la domanda di birra Exercise 8: Previsioni con vincolo Exercise 9: Effetti marginali medi Exercise 10: Grafici degli effetti Exercise 11: Modelli di risposta Probit Exercise 12: Un modello probit per la domanda di birra

Esercizio in corso

Exercise 13: Logistica vs. probit Exercise 14: Confronto tra modelli

The main goal of response modeling is to enable marketers to not only see a payoff for their actions today, but also tomorrow. In order to view this future payoff, a simple but reliable statistical model is required. In this last chapter, you will learn how to evaluate the predictive performance of logistic response models.

Exercise 1: Model selection Exercise 2: Extending the logistic response model Exercise 3: Summarizing the model Exercise 4: The deviance principle Exercise 5: Eliminating predictors Exercise 6: Predictive performance Exercise 7: Classifications Exercise 8: Model confusion Exercise 9: ROC curves Exercise 10: Model validation Exercise 11: Subsetting Exercise 12: Model training Exercise 13: Out-of-sample testing Exercise 14: Wrapping it up