Kinerja prediktif

Anda dapat menggunakan model Anda untuk membuat prediksi pada tingkat harga apa pun yang dipilih. Mendapatkan prediksi yang masuk akal bergantung pada kebenaran asumsi bahwa volume penjualan dan harga per unit berhubungan secara linear.

Anda akan menampilkan hubungan antara SALES dan PRICE dalam scatterplot sederhana menggunakan fungsi plot(). Sama seperti fungsi lm(), fungsi plot() juga dapat menggunakan argumen formula SALES ~ PRICE, dan akan membuat grafik yang diperlukan. Selanjutnya, Anda menambahkan prediksi model menggunakan fungsi abline(), yang menambahkan garis lurus yang ditentukan dalam bentuk intersep penjualan/kemiringan harga ketika diterapkan pada objek linear.model.

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

Petunjuk latihan

Tampilkan hubungan antara SALES dan PRICE menggunakan fungsi plot().
Sekali lagi, jelaskan SALES dengan PRICE dan simpan hasilnya ke objek bernama linear.model.
Tambahkan prediksi model dengan menerapkan fungsi abline() pada objek linear.model.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot SALES against PRICE
___(___ ~ ___, data = sales.data)

# Explain SALES by PRICE
linear.model <- ___(___ ~ ___, data = sales.data)

# Add the model predictions
___(___)

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: Dasar-dasar model respons pasar Exercise 2: Penjualan ritel Exercise 3: Memahami penjualan Exercise 4: Model respons linear Exercise 5: Model respons linear untuk penjualan Exercise 6: Membuat prediksi Exercise 7: Kinerja prediktif

Latihan Saat Ini

Exercise 8: Model respons nonlinier Exercise 9: Melinierkan fungsi nonlinier Exercise 10: Apa nilai tambahnya?

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: 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