Applied Statistics with Python

Applied Statistics with Python concentrates on applied and computational aspects of statistics, focussing on conceptual understanding and Python-based calculations. It compiles multiple aspects of applied statistics, teaching useful skills in statistics and computational science.

Applied Statistics with Python: Volume I: Introductory Statistics and Regression concentrates on applied and computational aspects of statistics, focusing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics courses at Touro University and Brooklyn College, this book compiles multiple aspects of applied statistics, teaching the reader useful skills in statistics and computational science with a focus on conceptual understanding. This book does not require previous experience with statistics and Python, explaining the basic concepts before developing them into more advanced methods from scratch. Applied Statistics with Python is intended for undergraduate students in business, economics, biology, social sciences, and natural science, while also being useful as a supplementary text for more advanced students.

Key Features:

• Concentrates on more introductory topics such as descriptive statistics, probability, probability distributions, proportion and means hypothesis testing, as well as one-variable regression
• The book's computational (Python) approach allows us to study Statistics much more effectively. It removes the tedium of hand/calculator computations and enables one to study more advanced topics
• Standardized sklearn Python package gives efficient access to machine learning topics

• Randomized homework as well as exams are provided in the author's course shell on My Open Math web portal (free)

  Autorentext

Leon Kaganovskiy is an Associate Professor at the Mathematics Department of Touro College. He received a M.S. in Theoretical Physics from Kharkov State University, and M.S. and PhD in Applied Mathematics from the University of Michigan. His most recent interest is in a broad field of Applied Statistics, and he has developed new courses in Bio-Statistics with R, Statistics for Actuaries with R, and Business Analytics with R. He teaches Statistics research courses at the Graduate Program in Speech-Language Pathology at Touro College.

Klappentext

Applied Statistics with Python concentrates on applied and computational aspects of statistics, focussing on conceptual understanding and Python-based calculations. It compiles multiple aspects of applied statistics, teaching useful skills in statistics and computational science.

Inhalt

Preface 1. Introduction 2. Descriptive Data Analysis 3. Probability 4. Probability Distributions 5. Inferential Statistics and Tests for Proportions 6. Goodness of Fit and Contingency Tables 7. Inference for Means 8. Correlation and Regression