If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data
Normal 0 false false false Clear, accessible, and teachable, "Stats: Modeling the World" leads with practical data analysis and graphics to engage students and get them thinking statistically from the start. Through updated, relevant examples and data--and the authors' signature "Think, Show, and Tell" problem-solving method--students learn what we can find in data, why we find it interesting, and how to report it to others. The new Fourth Edition is even more engaging than previous editions, builds on the innovative features that have made the first three editions so popular, and includes revisions designed to make it even easier for students to put the concepts of statistics together in a coherent whole.
Key Features of the Fourth Edition * Chapter 4, Probability, is now optional * Ten new smaller data sets, in addition to the hallmark Framingham Heart Study Data * Streamlined! - Organizing Data and Describing Data are now combined into a single chapter * Examples and Exercises include a stronger emphasis on statistical thinking and exploratory data analysis * Additional computer output from Minitab, Data Desk, JMP, SPSS, Resampling Stats, Maple V, and Mathematica
Stats: Data and Models, Third Edition, will intrigue and challenge students by encouraging them to think statistically and by emphasizing how statistics helps us understand the world. Praised by students and instructors alike for its readability and ease of comprehension, this text focuses on statistical thinking and data analysis. The authors draw from their wealth of consulting experience to craft compelling examples, which encourages students to learn how to reason with data. This book is organized into short chapters that concentrate on one topic at a time, offering instructors maximum flexibility in planning their courses. The text is appropriate for a one-or-two semester introductory statistics course and includes advanced topics, such as Analysis of Variance (ANOVA), Multiple Regression, and Nonparametrics.
Devoted to statistical thinking and learning, this special double issue reflects major developments within statistics education. During recent years, statistics has entered or gained increased prominence in mainstream mathematics curricula in many countries. Some aspects of the relationship between statistics education and mathematics education in general are illustrated, as is the crucial role of statistical education for responsible citizenship in modern society. The articles: * provide analyses of the development of children's statistical thinking, * discuss statistical thinking at a higher and more technical level, and * illustrate the issues central to the development of statistical education.
This book is a practical guide to problems that commonly arise when developing a machine learning project. The book's topics are: Exploratory data analysis Data Preparation Selecting best variables Assessing Model Performance More information on predictive modeling will be included soon. This book tries to demonstrate what it says with short and well-explained examples. This is valid for both theoretical and practical aspects (through comments in the code). This book, as well as the development of a data project, is not linear. The chapters are related among them. For example, the missing values chapter can lead to the cardinality reduction in categorical variables. Or you can read the data type chapter and then change the way you deal with missing values. You¿ll find references to other websites so you can expand your study, this book is just another step in the learning journey. It's open-source and can be found at http://livebook.datascienceheroes.com