Dummyvariabelen begrijpen

Ambachtelijk bier verkopen is zeer competitief. Meer zichtbaarheid in de winkel leidt meestal tot extra verkoop. Daarom maakt de brouwerij gebruik van point-of-sale-displays. De verkoopaantallen van Hoppiness zijn voor alle weken met en zonder displays geregistreerd.

Het is handig om te beginnen met het apart bekijken van log(SALES) voor DISPLAY- en geen-DISPLAY-activiteiten. Dat kan met de functie aggregate(). De functie aggregate() kan ook met formule-uitdrukkingen overweg, wat het gebruik erg praktisch maakt. Hier groepeert log(SALES) ~ DISPLAY log(SALES) volgens de niveaus in DISPLAY. Het argument FUN past een opgegeven functie toe op elk niveau. Je berekent opnieuw enkele eenvoudige beschrijvende maten met de functies mean(), min() en max().

Deze oefening maakt deel uit van de cursus

Responsmodellen bouwen in R

Cursus bekijken

Oefeninstructies

Bereken het gemiddelde van log(SALES) voor elk niveau in DISPLAY.
Bereken het minimum van log(SALES) voor elk niveau in DISPLAY.
Bereken het maximum van log(SALES) voor elk niveau in DISPLAY.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Mean log(SALES)
aggregate(___ ~ ___, FUN = ___, data = sales.data)

# Minimum log(SALES)
aggregate(___, FUN = ___, data = sales.data)

# Maximum log(SALES)
___(___, FUN = ___, data = sales.data)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Responsmodellen bouwen in R

SkillTag.level.intermediateSkillTag.label

4.8+

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: Uitreiding van het model deel 1: dummyvariabelen Exercise 2: Dummyvariabelen begrijpen

Huidige oefening

Exercise 3: Het effect van display op sales Exercise 4: Het effect van meerdere dummies op verkoop Exercise 5: En de prijs dan?Exercise 6: Ubreidingen van het model deel 2: Dynamische variabelen Exercise 7: Hoe vertraag je (lag)?Exercise 8: Vertraagde prijseffecten toevoegen Exercise 9: Meer lags Exercise 10: Wat is de toegevoegde waarde?Exercise 11: Hoeveel verlengingen zijn nodig?Exercise 12: Het model samenvatten Exercise 13: Onnodige voorspellers Exercise 14: Predictoren verwijderen Exercise 15: Predictors elimineren

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: Models for individual demand Exercise 2: Customer purchases Exercise 3: Summarizing the data Exercise 4: Competition Exercise 5: A linear probability model for beer demand Exercise 6: Logistic response models Exercise 7: A logistic model for beer demand Exercise 8: Bounded predictions Exercise 9: Average marginal effects Exercise 10: Effect plots Exercise 11: Probit response models Exercise 12: A probit model for beer demand Exercise 13: Logistic vs. probit Exercise 14: Model comparison

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