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Predictive analytics with SAS and Rc...

Mangrulkar, Ramchandra.

Predictive analytics with SAS and Rcore concepts, tools, and implementation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive analytics with SAS and Rby Ramchandra S Mangrulkar, Pallavi Vijay Chavan.
Reminder of title:	core concepts, tools, and implementation /
Author:	Mangrulkar, Ramchandra.
other author:	Vijay Chavan, Pallavi.
Published:	Berkeley, CA :Apress :2025.
Description:	xvii, 178 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Predictive analytics.
Online resource:	https://doi.org/10.1007/979-8-8688-0905-7
ISBN:	9798868809057$q(electronic bk.)

Predictive analytics with SAS and Rcore concepts, tools, and implementation /
Mangrulkar, Ramchandra.

Predictive analytics with SAS and Rcore concepts, tools, and implementation /[electronic resource] :by Ramchandra S Mangrulkar, Pallavi Vijay Chavan. - Berkeley, CA :Apress :2025. - xvii, 178 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1 Introduction to Analytics -- Chapter 2 Simple Linear Regression -- Chapter 3 Multiple Linear Regression -- Chapter 4 Multivariate Analysis and Prediction -- Chapter 5 Time Series Analysis.

Gain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background. The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models. After reading the book, you will be able to put predictive analytics principles into practice. What You Will Learn Understand modeling, estimating, and evaluating models for forecasting Implement Partial F-Test and Variable Selection Method Demonstrate each analysis model in R Studio and SAS Understand SLR and MLR Analysis models.

ISBN: 9798868809057$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0905-7doiSubjects--Topical Terms:

877232
Predictive analytics.

LC Class. No.: QA76.9.Q36

Dewey Class. No.: 001.42

Predictive analytics with SAS and Rcore concepts, tools, and implementation /
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505 0 $a Chapter 1 Introduction to Analytics -- Chapter 2 Simple Linear Regression -- Chapter 3 Multiple Linear Regression -- Chapter 4 Multivariate Analysis and Prediction -- Chapter 5 Time Series Analysis.
520 $a Gain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background. The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models. After reading the book, you will be able to put predictive analytics principles into practice. What You Will Learn Understand modeling, estimating, and evaluating models for forecasting Implement Partial F-Test and Variable Selection Method Demonstrate each analysis model in R Studio and SAS Understand SLR and MLR Analysis models.
650 0 $a Predictive analytics. $3 877232
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650 0 $a R (Computer program language) $3 210846
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Artificial Intelligence. $3 212515
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