000			02552nam a22002057a 4500
005			20251016172507.0
008			251016b |||||||| |||| 00| 0 eng d
020			_a9781032752631
082			_a519.50285 _bKAG
100			_aKaganovskiy, Leon _925095
245			_aApplied statistics with python: volume I: _bintroductory statistics and regression
260			_aBoca Raton _bCRC Press _c2025
300			_ax, 309 p.
365			_aGBP _b89.99
500			_aTable 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]
520			_aApplied 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)
650			_aStatistics--Probability _923391
650			_aStatistics--Python _925614
942			_cBK _2ddc
999			_c10511 _d10511