Сон, Хён Сок Создайте модель прогноза для акций с помощью регрессионного анализа и

Create forecast model for stock with regression analysis and R

Opis:

Linear regression analysis is a popular statistical method for building models to predict unknown values. That is, it estimates any unobserved value based on the relationship of existing data. Estimates by this method include many more than numbers. Understanding that sense is as important as building a model. This book explains a series of steps for building a regression model based on stock price data. In fact, mathematics, especially linear mathematical knowledge, is needed in order to explain the construction of regression models in detail. However, most of the necessary mathematical knowledge is empirically recognized, but the terms and expressions often interfere with their understanding. So, in this book, I used the computer programming language ‘R’ to facilitate the access of many of these empirically known basic knowledge. 'R' is intended for numerical analysis such as statistical analysis. It has a wide variety of tools for analyzing data and is a programming language that is developing continuously. First, it introduced R installation and basic grammar in Chapter 1. In Chapter 3, it introduced R matrix, which is applied in the construction of statistics, especially regression models. In Chapter 2, it introduced methods for generating stock price data needed to build a regression model. Chapters 4, 5, and 6 introduced the method of constructing a general regression model, using the language R, and introduced them inductively along with the necessary knowledge at each step. Chapter 7 explains how to supplement important but often overlooked problems that arise when there are many types of data to build a regression model. Section 8 summarizes the process of building an appropriate regression model using real data. It also introduced the process of comparing the observations with the results from the model. In regression analysis, there is a concept of point estimation that estimates a certain value and interval estimation that estimates the interval in which the estimation values belong. An important notion of confidence interval and prediction interval is used for interval estimation. Because this concept is an integral part of the interpretation of the results, it is also important to understand their inferencing process, so they are listed in Appendix A. Appendix B provides a bootstrap method that can be used when the number of data is small. Finally, many user-defined functions were used to introduce each method. These functions are introduced in Appendix C as a combination of functions provided by R. The knowledge about R that you need to build a regression model is mostly covered in this book. This book also contains most statistical knowledge related to the construction and interpretation of regression models. The mathematical expressions contained in this book are for regression model theory, but they are not an integral part of model construction and interpretation. Therefore, I hope that the contents introduced in this book will help you to understand your regression analysis and help you to create your own model.

Okładka: paperback

Liczba stron:196

Autor:Son, Hyun-Seok

Język: English: Published; English: Original Language; English

Data wydania: 2017-10-24

Waga: 1.281 gram

Wysokość: 0.5 cm

Szerokość: 8.5 cm

Długość: 11 cm

UWAGA: Kupując produkt na tej aukcji zgadzasz się na wydłużony termin realizacji wysyłki (10-14 dni roboczych). Podany towar pochodzi z zagranicznego magazynu, stąd wydłużony czas realizacji który podany jest obok.