Menghapus prediktor

Untuk mendukung keputusan Anda, Anda membandingkan kecocokan model dari model maksimal dengan model yang mengecualikan efek kupon tertunda (lagged) menggunakan AIC-nya.

Anda dapat memperoleh nilai AIC dengan menggunakan fungsi AIC() pada objek model terkait. Ini mudah untuk model maksimal yang diberikan oleh objek extended.model. Untuk mendapatkan nilai AIC bagi model yang mengecualikan Coupon.lag, pertama-tama Anda perlu menerapkan fungsi update() pada objek extended.model. Selain memperbarui model untuk memasukkan prediktor tambahan, Anda juga dapat menghapus prediktor. Anda hanya perlu menaruh tanda minus di depan prediktor terkait.

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

Petunjuk latihan

Dapatkan nilai AIC untuk objek extended.model dengan menggunakan fungsi AIC().
Perbarui objek extended.model dengan menghapus Coupon.lag menggunakan fungsi update(). Dapatkan AIC untuk model yang telah diperbarui dengan menggunakan fungsi AIC().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Obtain the AIC
___(extended.model)

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

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Perluasan model bagian 1: Variabel dummy Exercise 2: Memahami variabel dummy Exercise 3: Dampak display terhadap penjualan Exercise 4: Dampak beberapa dummy terhadap penjualan Exercise 5: Bagaimana dengan harga?Exercise 6: Perluasan model bagian 2: Variabel dinamis Exercise 7: Cara membuat lag?Exercise 8: Menambahkan efek harga terdorong (lag)Exercise 9: Lag tambahan Exercise 10: Apa nilai tambahnya?Exercise 11: Berapa banyak ekstensi yang diperlukan?Exercise 12: Merangkum model Exercise 13: Prediktor yang tidak diperlukan Exercise 14: Menghapus prediktor

Latihan Saat Ini

Exercise 15: Mengeliminasi prediktor

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