Applied statistics with python: volume I: introductory statistics and regression

Table of contents:
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

[https://www.routledge.com/Applied-Statistics-with-Python-Volume-I-Introductory-Statistics-and-Regression/Kaganovskiy/p/book/9781032751931]

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

(https://www.routledge.com/Applied-Statistics-with-Python-Volume-I-Introductory-Statistics-and-Regression/Kaganovskiy/p/book/9781032751931)