دانلود کتاب آمار برای دانشمندان – یک راهنمای دقیق برای تخقیق داده محور

This short book has originated from lectures I gave at various universities that have helped me identify the concepts in statistics that most scientists struggle with. Most of them have an intuitive understanding of what, for example, mean and median are, but few can tell when to use which, for example, or can explain how to interpret the coefficient R2.. This book is a summary of those concepts, described in a short and concise way, enriched with tips and rigorous explanations. It is important to understand that the goal of this book is not to provide a complete course in statistics. There are many books that do this already. My goal is to give researchers and practitioners a short text that is easy to read to help them understand the most important concepts in statistics and especially how to use them properly. Readers are encouraged to continue the study of statistics, an incredibly interesting branch of mathematics that helps us understand the world around us. This book contains enough material for a short introductory course for students at an undergraduate level. You will find some sections of this book marked with a star . at the beginning of the title. That means that the section is more mathematically challenging and can be safely skipped by those with less mathematical know-how or interest. This book is structured to provide an accessible overview of statistics and data analysis for scientific research. It begins with basic concepts, including an explanation of random variables, outcome spaces, and the difference between descriptive and inferential statistics. It continues with data types, measures of central tendency, of dispersion, and of positions. The discussion continues with a discussion of outliers and various methods to define them. Then the book introduces more complex topics like distributions, hypothesis testing, and regression analysis. Each chapter builds on the previous one, introducing more complex statistical techniques in a step-by-step manner, making it suitable for readers ranging from beginners to those needing a quick refresher. The choice of topics, as often, is somewhat subjective. But I selected the concepts that all those interested in using statistics should know about. Some topics (such as hypothesis testing) are not discussed at length, since a complete treatment of the topics would require not only more space but also the student having some more background in statistics and mathematics (e.g., in calculus). The goal of these chapters is to give the student enough understanding of the main i dea and inspire them to study the concepts deeper. In the book you will find four types of boxes: definitions, tips, warnings, and examples. The meaning should be clear. In definitions boxes, you will find definitions of concepts, so it is easier to find them. In tips you will find, as the name suggests, tips and suggestions that I hope will make specific concepts or applications clearer. In warnings I try to highlight tricky cases in the application or interpretation of methods or concepts. And finally, in examples I try to give some examples to make concepts clearer. Two books have significantly influenced my journey in learning statistics. One is the book Probability and Statistical Inference by Hogg, Tanis, and Zimmerman now in its 10th edition [1]. In my opinion, it is the perfect book for (almost) beginners. A more advanced book, but beautiful in its rigor and choice of topics, is the one by Casella and Berger Statistical Inference [2]. A masterpiece that every scientist should have on his or her bookshelf. If you are looking to delve deeper into statistics after reading this short introduction, you cannot go wrong with those two books. If you are a beginner, after this book, I suggest you continue with the book by Hogg et al.