Statistics With R Programming A Beginner S Guide For Jntu Kakinada
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Statistics With R Programming A Beginner S Guide For Jntu Kakinada
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Author : Mohan, Murali T.
language : en
Publisher: S. Chand Publishing
Release Date :
Statistics With R Programming A Beginner S Guide For Jntu Kakinada written by Mohan, Murali T. and has been published by S. Chand Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Statistics with R Programming - A Beginner's Guide
Beginner S Guide For Data Analysis Using R Programming
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Author : Jeeva Jose
language : en
Publisher: KHANNA PUBLISHING HOUSE
Release Date :
Beginner S Guide For Data Analysis Using R Programming written by Jeeva Jose and has been published by KHANNA PUBLISHING HOUSE this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
R programming is an efficient tool for statistical analysis of data. Data science has become critical to each field and the popularity of R is skyrocketing. Organization as large and diverse as Google, Facebook, Microsoft, Bank of America, Ford Motor Company, Mozilla, Thomas Cook, The New York Times, The National Weather Service, Twitter, ANZ Bank, Uber, Airbnb etc . have turned to R for reporting, analyzing and visualization of data, this book is for students and professionals of Mathematics, Statistics, Physics, Chemistry, Biology, Social Science and Medicine, Business, Engineering, Software, Information Technology, Sales, Bio Informatics, Pharmacy and any one, where data needs to be analyzed and represented graphically.
Learn R Programming In 24 Hours
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Author : Alex Nordeen
language : en
Publisher: Guru99
Release Date : 2020-10-31
Learn R Programming In 24 Hours written by Alex Nordeen and has been published by Guru99 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-31 with Computers categories.
R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference?
Statistics With R
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Author : Robert Stinerock
language : en
Publisher: SAGE
Release Date : 2022-11-26
Statistics With R written by Robert Stinerock and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-26 with Reference categories.
With a clear step-by-step approach explained using real world examples, this book provides the practical skills you need to use statistical methods in your research from an expert with over 30 years of teaching experience.
Statistical Analysis With R
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Author : John M. Quick
language : en
Publisher:
Release Date : 2010
Statistical Analysis With R written by John M. Quick and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematical statistics categories.
Statistics With R
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Author : Robert Stinerock
language : en
Publisher: SAGE Publications Limited
Release Date : 2018-02-27
Statistics With R written by Robert Stinerock and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-27 with Social Science categories.
***Choice Outstanding Academic Title Award Winner*** The dynamic, student focused textbook provides step-by-step instruction in the use of R and of statistical language as a general research tool. It is ideal for anyone hoping to: Complete an introductory course in statistics Prepare for more advanced statistical courses Gain the transferable analytical skills needed to interpret research from across the social sciences Learn the technical skills needed to present data visually Acquire a basic competence in the use of R. The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed pedagogy and jargon-free definitions. Theory is introduced as an accessible and adaptable tool and is always contextualized within the pragmatic context of real research projects and definable research questions. Author Robert Stinerock has also created a wide range of online resources, including: R scripts, complete solutions for all exercises, data files for each chapter, video and screen casts, and interactive multiple-choice quizzes.
R Statistical Application Development By Example Beginner S Guide
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Author : Prabhanjan Narayanachar Tattar
language : en
Publisher: Packt Pub Limited
Release Date : 2013
R Statistical Application Development By Example Beginner S Guide written by Prabhanjan Narayanachar Tattar and has been published by Packt Pub Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.
Full of screenshots and examples, this Beginner's Guide by Example will teach you practically everything you need to know about R statistical application development from scratch. You will begin learning the first concepts of statistics in R which is vital in this fast paced era and it is also a bargain as you do not need to do a preliminary course on the subject.
Statistical Analysis With R
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Author : John M. Quick
language : en
Publisher:
Release Date : 2010
Statistical Analysis With R written by John M. Quick and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with MATHEMATICS categories.
Annotation This is a practical, step by step guide that will help you to quickly become proficient in the data analysis using R. The book is packed with clear examples, screenshots, and code to carry on your data analysis without any hurdle. If you are a data analyst, business or information technology professional, student, educator, researcher, or anyone else who wants to learn to analyze the data effectively then this book is for you. No prior experience with R is necessary. Knowledge of other programming languages, software packages, or statistics may be helpful, but is not required.
Learn R Programming In 1 Day
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Author : Krishna Rungta
language : en
Publisher:
Release Date : 2019-09-10
Learn R Programming In 1 Day written by Krishna Rungta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with categories.
R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, d104 File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference?
Java Programming A Comprehensive Beginner S Guide
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Author : Dr.Bomma Rama Krishna
language : en
Publisher: PND Publishers
Release Date : 2023-06-29
Java Programming A Comprehensive Beginner S Guide written by Dr.Bomma Rama Krishna and has been published by PND Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-29 with Computers categories.
In the world of programming, Object-Oriented (OO) paradigm has gained significant importance and has become the foundation for designing and developing complex software systems. This paradigm provides a systematic approach to organizing and managing code by modeling real-world entities as objects and defining their behaviors through classes. This book aims to provide a comprehensive understanding of the OO paradigm using Java as the primary programming language. Java is a popular and widely used language that fully supports the principles and concepts of OO programming. It offers a robust set of features, including inheritance, polymorphism, encapsulation, and abstraction, which are essential for building scalable and maintainable software applications. The book starts with an introduction to the fundamental concepts of OO programming, including the concept of agents and their role in viewing the world. It then delves into the basics of Java, covering its timeline, features, syntax, data types, variables, and operators. The concept of classes and objects is introduced, along with topics like constructors, access control, method overloading, parameter passing, and recursion. Next, the book explores the concepts of strings and arrays in Java, followed by an in-depth discussion on inheritance, packages, and interfaces. Inheritance allows for the creation of hierarchical abstractions, and the book explores different types of inheritance, such as specialization, specification, construction, and extension. It also covers the benefits and costs associated with inheritance and explains the member access rules and the use of keywords like "super" and "final." The book then moves on to discuss packages and interfaces, which provide mechanisms for organizing and structuring code. It explains how to define, create, and access packages and how to define interfaces in Java. Exception handling and multithreading are vital aspects of modern programming, and Chapter 3 focuses on these topics. It covers the concepts of exception handling, including try-catch blocks, throws, and finally clauses. Custom exceptions and the creation of exception subclasses are also discussed. The chapter then delves into multithreading, explaining the differences between multithreading and multitasking, the thread class hierarchy, thread life cycle, and various aspects of thread creation, synchronization, daemon threads, and thread groups. Chapter 4 introduces the Abstract Window Toolkit (AWT), which provides the foundation for building graphical user interfaces (GUIs) in Java. It covers the AWT hierarchy and explains how to create a GUI using the Frame class. Event handling is also discussed, including event handling code and various AWT components such as buttons, labels, checkboxes, lists, text fields, and more. Swings, which is an advanced GUI toolkit in Java, is the focus of Chapter 5. The chapter explores the history of Swings, compares it with AWT, and explains the Model-View-Controller (MVC) architecture. It then covers commonly used Swing components and containers, including buttons, labels, text fields, checkboxes, radio buttons, and more. Layout managers, which are essential for organizing and positioning components within a container, are also discussed. Finally, Chapter 6 presents several Java projects to apply the concepts learned throughout the book. These projects include a moving balls application using threads and GUI, a simple image viewer, a basic calculator, and the implementation of a simple notepad. By the end of this book, readers will have a solid understanding of the principles and concepts of the OO paradigm, as well as the ability to develop Java applications using object-oriented techniques. The book is designed to be a valuable resource for beginners and intermediate programmers looking to enhance their understanding and skills in object-oriented programming with Java.