Modellen vergelijken

Om de price.ratio-coëfficiënt van het probit.model te kunnen interpreteren, bereken je het effect van een gemiddelde eenheidsverandering op de latente geneigdheid met de functie margins(). Je controleert of het effect voor het probit.model anders kan zijn door de functie margins() opnieuw aan te roepen op het object logistic.model.

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Responsmodellen bouwen in R

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Oefeninstructies

Bepaal het marginale effect voor price.ratio van het logistic.model met de functie margins().
Bepaal het marginale effect voor price.ratio van het probit.model met de functie margins().

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# Logistic model


# Probit model

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Deze oefening maakt deel uit van de cursus

Responsmodellen bouwen in R

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Begin de cursus gratis

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: Modellen voor individuele vraag Exercise 2: Aankopen van klanten Exercise 3: De gegevens samenvatten Exercise 4: Concurrentie Exercise 5: Een lineair waarschijnlijkheidsmodel voor biervraag Exercise 6: Logistische responsmodellen Exercise 7: Een logistisch model voor biervraag Exercise 8: Begrensde voorspellingen Exercise 9: Gemiddelde marginale effecten Exercise 10: Effectplots Exercise 11: Probit-responsmodellen Exercise 12: Een probitmodel voor biervraag Exercise 13: Logistisch vs. probit Exercise 14: Modellen vergelijken

Huidige oefening

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