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Daniel J. Denis
Applied Univariate, Bivariate, and Multivariate Statistics Using Python
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Applied Univariate, Bivariate, and Multivariate Statistics Using Python A Beginner’s Guide to Advanced Data Analysis
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Contents
List of Tables
List of Illustrations
Guide
Pages
Preface
Statistical Knowledge vs. Software Knowledge
Mathematical vs. “Conceptual” Understanding
Advice for Instructors
1 A Brief Introduction and Overview of Applied Statistics CHAPTER OBJECTIVES
1.1 How Statistical Inference Works
1.2 Statistics and Decision-Making
1.3 Quantifying Error Rates in Decision-Making: Type I and Type II Errors
1.4 Estimation of Parameters
1.5 Essential Philosophical Principles for Applied Statistics
1.6 Continuous vs. Discrete Variables
1.6.1 Continuity Is Not Always Clear-Cut
1.7 Using Abstract Systems to Describe Physical Phenomena: Understanding Numerical vs. Physical Differences
1.8 Data Analysis, Data Science, Machine Learning, Big Data
1.9 “Training” and “Testing” Models: What “Statistical Learning” Means in the Age of Machine Learning and Data Science
1.10 Where We Are Going From Here: How to Use This Book
Review Exercises
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