This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R as well as supplementary material that will enable the reader to quickly adapt all methods to their own applications.
Introduction to Statistics and Data Analysis
The authors guide you through an intuition-based learning process that stresses interpretation and communication of statistical information.
A Hands-On Approach to Teaching Introductory StatisticsExpanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and ...
With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.
Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics.
In Introduction to Statistics and Data Analysis, Bob Lockhart emphasizes the link between statistical techniques and scientific discovery by focusing on evaluation and comparison of models.
This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful.
Advanced topics of the book go beyond the basics and cover more complex issues in laboratory investigations with examples, including association studies such as correlation and regression analysis with laboratory applications such as ...
Mitchell, C. et al. Genetic differential sensitivity to social environments: Implications for research. Ann. Rev. of Psy. 65, 41–70 (2014). Roisman, G. I. et al. Distinguishing differential susceptibility from diathesis-stress: ...
Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data ...