Parameters interpreteren

De parameters van het model vertellen ons iets over hoe aantrekkelijk de kenmerken van de alternatieven zijn. Zo geeft de parameter voor convertyes aan hoeveel mensen een cabriodak verkiezen boven het basisniveau, convertno. Als de parameter voor convertyes positief is, geven mensen gemiddeld de voorkeur aan auto’s met een cabriodak. Is hij negatief, dan hebben auto’s zonder cabriodak de voorkeur.

Gebruik summary() om de modelparameters voor het object m1 te bekijken. Interpreteer daarna de parameter voor convertyes om te bepalen of cabrio’s meer of minder gewenst zijn dan niet-cabrio’s.

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Our goal for this chapter is to get you through the entire choice modeling process as quickly as possible, so that you get a broad understanding of what we can do with choice models and how the choice modeling process works. The main idea here is that we can use a choice model to understand how customers' product choices depend on the features of those products. Do sportscar buyers prefer manual transmissions to automatic? By how much? In order to give you an overview, we will skip over many of the details. In later chapters, we will go back and cover important issues in preparing data, specifying and interpreting models and reporting your findings, so that you are fully prepared to use these methods with your own choice data.

Exercise 1: Waarom keuze?Exercise 2: Keuzedata Exercise 3: Keuzedata verkennen Exercise 4: De niveaus van een factor vinden Exercise 5: Een keuze-observatie inspecteren Exercise 6: Wat kozen mensen?Exercise 7: Een keuzemodel fitten en interpreteren Exercise 8: Een keuzemodel fitten Exercise 9: Parameters interpreteren

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Exercise 10: Keuzemodellen gebruiken om beslissingen te nemen Exercise 11: Keuzeaandelen voorspellen Exercise 12: Keuzeaandelen plotten

There are many different places to get choice data and different ways it can be formatted. In this chapter, we will take data that is provided in several alternative formats and learn how to get it into shape for choice modeling. We will also discuss how you can build a survey to collect your own choice data.

Exercise 1: Assembling choice data Exercise 2: How many choices do we have?Exercise 3: Attributes and levels Exercise 4: Inspect a single choice Exercise 5: Converting from wide to long Exercise 6: Convert from wide to long Exercise 7: Sort the long data Exercise 8: Change the Selection variable Exercise 9: Choice data in two files Exercise 10: Inspect a choice in two files Exercise 11: Merging two files Exercise 12: Visualizing choice data Exercise 13: What types of chocolate do people choose?Exercise 14: Do people choose lower prices?Exercise 15: Designing a conjoint survey

In this chapter, we take deeper dive into estimating choice models. To give you a foundation for thinking about choice models, we will focus on how the multinomial logit model converts the product features into a prediction for what the decision maker will choose. This will give you a framework for making decisions about which features to include in your model.

Exercise 1: Choice models - under the hood Exercise 2: Create mlogit.data object Exercise 3: Fit a choice model Exercise 4: Remove the intercept Exercise 5: Interpreting choice model parameters Exercise 6: Which chocolate brand is most preferred?Exercise 7: Computing willingness-to-pay Exercise 8: Price as a factor Exercise 9: Likelihood-ratio test Exercise 10: Intercepts and interactions Exercise 11: Interaction between brand and type Exercise 12: Interaction between price and trial Exercise 13: Predicting shares Exercise 14: Predicting shares for the racer segment Exercise 15: Predict shares for chocolate bars Exercise 16: Plot shares for chocolate bars

Different people have different tastes and preferences. This seems intuitively obvious, but there is also extensive research in marketing showing that this is true. This chapter covers choice models where we assume that different decision makers have different preferences that influence their choices. When our models recognize that different consumers have different preferences, they tend to make larger share predictions for niche products that appeal to a subset of consumers. Hierarchical models are used in most commercial choice modeling applications, so it is important to understand how they work.

Exercise 1: What is a hierarchical choice model?Exercise 2: How many people answered the chocolate suvey?Exercise 3: Chocolate model with random price coefficient Exercise 4: Heterogeneity in preferences for other features Exercise 5: Setting effects codes Exercise 6: Chocolate model with all coefficients random Exercise 7: Interpreting hierarchical model parameters Exercise 8: How do people value white chocolate?Exercise 9: Predicting shares with hierarchical models Exercise 10: Predicting shares for chocolates Exercise 11: Goodbye and good luck!