Predictive Analytics with SAS and R
Description
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
Who This Book Is For
Students and professionals in the field of data analysis and intelligence applications
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Persons
Dr. Ramchandra Sharad Mangrulkar is a Professor in the Department of Information Technology at Dwarkadas J. Sanghvi College of Engineering in Mumbai, India. He holds various memberships in professional organizations such as IEEE, ISTE, ACM, and IACSIT. He has established himself as a knowledgeable and skilled professional in his field. He has also obtained certifications such as Certified Network Security Specialist (ICSI - CNSS) from ICSI, UK. He has a strong publication record with 126 publications. Dr. Mangrulkar is proficient in several technologies and tools, including Microsoft's Power BI, Power Automate, Power Query, Power Virtual Agents, Google's Dialog Flow, Data Analytics Models and Overleaf.
Dr. Pallavi Vijay Chavan is an Associate Professor in the Department of Information Technology at Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, Navi Mumbai, MH, India. She has been in academics for the past 17 years and has worked in the areas of computing theory, data science, data analytics, and network security. In her academic journey, she has published research work in the data science and security domains with reputed publishers including Springer, Elsevier, CRC Press, and Inderscience.
Content
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.
