Yordayıcıları çıkarmak

Kararını desteklemek için, en kapsamlı modelin uyumunu gecikmeli kupon etkisini hariç tutan modelle AIC üzerinden karşılaştırırsın.

AIC değerini, ilgili model nesnesi üzerinde AIC() fonksiyonunu kullanarak alabilirsin. Bu, extended.model nesnesiyle verilen en kapsamlı model için kolaydır. Coupon.lag değişkenini hariç tutan modelin AIC değerini almak için önce extended.model nesnesine update() fonksiyonunu uygulaman gerekir. Bir modele ek bir yordayıcı ekleyerek güncellemenin yanı sıra, bir yordayıcıyı da çıkarabilirsin. Bunun için ilgili yordayıcının başına eksi işareti koyman yeterlidir.

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

extended.model nesnesi için AIC() fonksiyonunu kullanarak AIC değerini al.
extended.model nesnesini update() fonksiyonunu kullanarak Coupon.lag değişkenini çıkaracak şekilde güncelle. Güncellenmiş model için AIC() fonksiyonuyla AIC değerini al.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Obtain the AIC
___(extended.model)

# Update the AIC by single term deletion
___(___(extended.model, . ~ . ___))

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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 genişletme bölüm 1: Kukla değişkenler Exercise 2: Sahte değişkenleri (dummy variables) anlama Exercise 3: Teşhirin satışlar üzerindeki etkisi Exercise 4: Birden çok sahte değişkenin satışlara etkisi Exercise 5: Peki ya fiyat?Exercise 6: Model genişletmeleri bölüm 2: Dinamik değişkenler Exercise 7: Lag nasıl oluşturulur?Exercise 8: Gecikmeli fiyat etkilerini ekleme Exercise 9: Daha fazla gecikme Exercise 10: Katılan değer ne?Exercise 11: Kaç uzatma gerekiyor?Exercise 12: Modeli özetleme Exercise 13: Gereksiz yordayıcılar Exercise 14: Yordayıcıları çıkarmak

Geçerli Egzersiz

Exercise 15: Yordayıcıları elemek

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