Beginner’s Guide to Correlation Analysis


Book Description

**Beginner’s Guide to Correlation Analysis: Learn The One Reason Your Correlation Results Are Probably Wrong** Ever wondered why your correlation results seem off? There's one crucial factor you might be missing. But don't worry, "Beginner’s Guide to Correlation Analysis" is here to help you get it right! **Why you need this book:** - **Clear understanding:** Learn the fundamental principles of correlation analysis in an easy-to-follow way. - **Avoid common mistakes:** Discover the most common reason why correlation results are often incorrect and how to fix it. - **Practical guidance:** Get practical tips on how to choose the right methods for analyzing your data. - **No jargon:** Enjoy explanations in plain English, without any complicated statistical terminology. - **Visual examples:** Benefit from visually intuitive examples that make the concepts easy to grasp. - **Beginner-friendly:** Perfect for those new to statistics, no prior experience required. Correlation is all about understanding how two variables move together. If one changes, the other is likely to change as well. But many people get their correlation results wrong because they overlook a critical aspect. This book will show you what that is and how to correct it. In "Beginner’s Guide to Correlation Analysis," you'll learn to work with your data effectively, select the right statistical tools, and interpret your results accurately. By focusing on the key elements that often trip people up, this guide ensures you won't make the same mistakes. You'll also find visually engaging examples that simplify complex ideas, making them easier to understand. Whether you're just starting out or need a refresher, this book is designed to be accessible and helpful for everyone. Ready to master correlation analysis and get accurate results? Equip yourself with the knowledge and skills to confidently analyze your data. Grab your copy of "Beginner’s Guide to Correlation Analysis" today and start getting your correlations right!




Beginner's Guide for Data Analysis using R Programming


Book Description

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.




A Beginner's Guide to Structural Equation Modeling


Book Description

The second edition features: a CD with all of the book's Amos, EQS, and LISREL programs and data sets; new chapters on importing data issues related to data editing and on how to report research; an updated introduction to matrix notation and programs that illustrate how to compute these calculations; many more computer program examples and chapter exercises; and increased coverage of factors that affect correlation, the 4-step approach to SEM and hypothesis testing, significance, power, and sample size issues. The new edition's expanded use of applications make this book ideal for advanced students and researchers in psychology, education, business, health care, political science, sociology, and biology. A basic understanding of correlation is assumed and an understanding of the matrices used in SEM models is encouraged.




Practical Statistics


Book Description

Making statistics—and statistical software—accessible and rewarding This book provides readers with step-by-step guidance on running a wide variety of statistical analyses in IBM® SPSS® Statistics, Stata, and other programs. Author David Kremelberg begins his user-friendly text by covering charts and graphs through regression, time-series analysis, and factor analysis. He provides a background of the method, then explains how to run these tests in IBM SPSS and Stata. He then progresses to more advanced kinds of statistics such as HLM and SEM, where he describes the tests and explains how to run these tests in their appropriate software including HLM and AMOS. This is an invaluable guide for upper-level undergraduate and graduate students across the social and behavioral sciences who need assistance in understanding the various statistical packages.




Applying Regression and Correlation


Book Description

Takes a look at applying regression analysis in the behavioural sciences by introducing the reader to regression analysis through a simple model-building approach.




Learning Statistics with R


Book Description

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com




Cause and Correlation in Biology


Book Description

This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.




Correlation Is Not Causation


Book Description

**Correlation Is Not Causation: Learn How to Avoid the 5 Traps That Even Pros Fall Into** Ever heard someone confidently declare that because two things are correlated, one must cause the other? We've all been there. "Correlation Is Not Causation: Learn How to Avoid the 5 Traps That Even Pros Fall Into" is your friendly, chatty guide to understanding the nuances of correlation and causation, and how to avoid the common mistakes that even experts can make. **Benefits of this book:** - **Master the basics:** Learn why correlation doesn’t imply causation with simple, clear explanations. - **Identify common pitfalls:** Understand the five traps that can mislead you into thinking correlation equals causation. - **Develop critical thinking:** Enhance your ability to critically analyze data and avoid false conclusions. - **Easy to understand:** Written in plain English, perfect for beginners and those without a technical background. - **Visual examples:** Packed with intuitive, visual examples to make complex concepts easy to grasp. - **Practical strategies:** Get actionable strategies to correctly interpret data and identify true causal relationships. We often look for patterns and explanations in the world around us. When two things seem related, it's tempting to conclude that one causes the other. This book dives into the reasons why this assumption can be misleading and how to avoid falling into that trap. In "Correlation Is Not Causation," you'll discover the five alternatives to one variable being the direct cause of another when a correlation is found. We break down each alternative and show you how to systematically test for them, ensuring you understand the real relationship between variables. From formulating a plan to analyze data to interpreting results without falling into common pitfalls, this book provides a comprehensive yet accessible guide. With no statistical jargon, it's perfect for anyone looking to improve their data literacy. Ready to navigate the world of data with confidence? Equip yourself with the knowledge to discern true causal relationships and avoid misleading correlations. Get your copy of "Correlation Is Not Causation" today and start making smarter, data-driven decisions!




A Beginner’s Guide to Statistics for Criminology and Criminal Justice Using R


Book Description

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).




A Beginner's Guide to Structural Equation Modeling


Book Description

This textbook presents a basic introduction to structural equation modeling (SEM) and focuses on the conceptual steps to be taken in analysing conceptual models.