Statistical Analysis with Python For Dummies

Höfundur: Joseph Schmuller (Útgáfa: 1)
Statistical Analysis with Python For Dummies

Kaup valmöguleikar

Wrangle stats as you learn how to graph, analyze, and interpret data with Python Statistical Analysis with Python For Dummies introduces you to the tool of choice for digging deep into data to inform business decisions. Even if you're new to coding, this book unlocks the magic of Python and shows you how to apply it to statistical analysis tasks. You'll learn to set up a coding environment and use Python's libraries and functions to mine data for correlations and test hypotheses.

You'll also get a crash course in the concepts of probability, including graphing and explaining your results. Part coding book, part stats class, part business analyst guide, this book is ideal for anyone tasked with squeezing insight from data. Get clear explanations of the basics of statistics and data analysis Learn how to summarize and analyze data with Python, step by step Improve business decisions with objective evidence and analysis Explore hypothesis testing, regression analysis, and prediction techniques This is the perfect introduction to Python for students, professionals, and the stat-curious.

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Útgefandi
Wiley Professional Development (P&T)
ISBN
9781394370344
Print ISBN
9781394370320
Format
ePub
Útgáfa
1
Höfundar
Joseph Schmuller
Tungumál
English
Útgefið
2025-11-13
Prent takmörkun á líftíma
100
Prent takmörkun
10
Afritunar takmörkun
2

Kaflar

  • Cover
  • Table of Contents
  • Title Page
  • Copyright
  • Introduction
  • About This Book
  • Similarity with These Other For Dummies Books
  • What You Can Safely Skip
  • Foolish Assumptions
  • How This Book Is Organized
  • Icons Used in This Book
  • Where to Go from Here
  • Part 1: Getting Started with Statistical Analysis with Python
  • Chapter 1: Data, Statistics, and Decisions
  • The Statistical (and Related) Notions You Just Have to Know
  • Inferential Statistics: Testing Hypotheses
  • Chapter 2: Python: What It Does and How It Does It
  • Introducing Colab
  • Exploring the Colab Environment
  • Introducing Python
  • Working with Python Functions
  • Checking Out Python Libraries
  • Going Round and Round with Looping
  • Considering Conditionals
  • Comprehending List Comprehension
  • Defining Your Own Functions
  • Wrapping Up
  • Part 2: Describing Data
  • Chapter 3: Getting Graphic
  • Getting the Data
  • Creating a Histogram
  • Barhopping
  • Slicing the Pie
  • The Plot of Scatter
  • Of Boxes and Whiskers
  • Continuous Variables
  • Wrapping Up
  • Chapter 4: Finding Your Center
  • Means: The Lure of Averages
  • The Average in Python
  • Medians: Caught in the Middle
  • The Median in Python
  • Statistics à la Mode
  • The Mode in Python
  • Chapter 5: Deviating from the Average
  • Measuring Variation
  • Variance in Python
  • Back to the Roots: Standard Deviation
  • Standard Deviation in Python
  • Conditions, Conditions, Conditions …
  • Chapter 6: Meeting Standards and Standings
  • Catching Some Z’s
  • z-Scores in Python
  • Where Do You Stand?
  • Chapter 7: Summarizing It All
  • How Many?
  • The High and the Low
  • Living in the Moments
  • Tuning in the Frequency
  • Summarizing a DataFrame
  • Chapter 8: What’s Normal?
  • Hitting the Curve
  • Working with Normal Distributions
  • A Distinguished Member of the Family
  • Part 3: Drawing Conclusions from Data
  • Chapter 9: The Confidence Game: Estimation
  • Understanding Sampling Distributions
  • An EXTREMELY Important Idea: The Central Limit Theorem
  • Confidence: It Has Its Limits!
  • Finding Confidence Limits for a Mean
  • Fit to a t
  • Chapter 10: One-Sample Hypothesis Testing
  • Hypotheses, Tests, and Errors
  • Hypothesis Tests and Sampling Distributions
  • Catching Some Z’s Again
  • z-Testing in Python
  • t for One
  • t Testing in Python
  • Working with t-Distributions
  • Visualizing t-Distributions
  • Testing a Variance
  • Testing a Variance in Python
  • Working with Chi-Square Distributions
  • Visualizing Chi-Square Distributions
  • Chapter 11: Two-Sample Hypothesis Testing
  • Hypotheses Built for Two
  • Sampling Distributions Revisited
  • t for Two
  • t-Testing in Python
  • A Matched Set: Hypothesis Testing for Paired Samples
  • Paired Sample t-Testing in Python
  • Testing Two Variances
  • Working with F-Distributions
  • Visualizing F-Distributions
  • Chapter 12: Testing More than Two Samples
  • Testing More than Two
  • ANOVA in Python
  • After the ANOVA
  • Another Kind of Hypothesis, Another Kind of Test
  • Getting Trendy
  • Trend Analysis in Python
  • Chapter 13: More Complicated Testing
  • Cracking the Combinations
  • Two-Way ANOVA in Python
  • Visualizing the Two-Way Results
  • Two Kinds of Variables … at Once
  • After the Analysis
  • Multivariate Analysis of Variance
  • Chapter 14: Regression: Linear, Multiple, and the General Linear Model
  • The Plot of Scatter
  • Graphing Lines
  • Regression: What a Line!
  • Linear Regression in Python
  • Juggling Many Relationships at Once: Multiple Regression
  • ANOVA: Another Look
  • Analysis of Covariance: The Final Component of the GLM
  • But Wait — There's More
  • Chapter 15: Correlation: The Rise and Fall of Relationships
  • Scatterplots, Again
  • Understanding Correlation
  • Correlation and Regression
  • Testing Hypotheses About Correlation
  • Correlation in Python
  • Multiple Correlation
  • Partial Correlation
  • Partial Correlation in Python
  • Semipartial Correlation
  • Semipartial Correlation in Python
  • Chapter 16: Curvilinear Regression: When Relationships Get Complicated
  • What Is a Logarithm?
  • What Is e?
  • Power Regression
  • Exponential Regression
  • Logarithmic Regression
  • Polynomial Regression: A Higher Power
  • Which Model Should You Use?
  • Part 4: Working with Probability
  • Chapter 17: Introducing Probability
  • What Is Probability?
  • Compound Events
  • Conditional Probability
  • Large Sample Spaces
  • Python Functions for Counting Rules
  • Random Variables: Discrete and Continuous
  • Probability Distributions and Density Functions
  • The Binomial Distribution
  • The Binomial and Negative Binomial in Python
  • Hypothesis Testing with the Binomial Distribution
  • More on Hypothesis Testing: Python versus Tradition
  • Chapter 18: Introducing Modeling
  • Modeling a Distribution
  • A Simulating Discussion
  • Chapter 19: Probability Meets Regression: Logistic Regression
  • Getting the Data
  • Doing the Analysis
  • Part 5: The Part of Tens
  • Chapter 20: Ten Tips for R Veterans
  • Python Libraries Are (Somewhat) Different from R Libraries
  • Python's Statistics Functions Live in Libraries
  • In Python, Distributions Also Live in Libraries
  • Dot Notation in Python Is Important
  • Dot in Python is Much Like $ in R
  • Two Important Libraries: NumPy and Pandas
  • Use the Dictionary
  • Learn the statsmodels Library
  • Where Are the Vectors?
  • A Python Grammar of Graphics
  • Chapter 21: Ten Valuable Python Resources
  • Python.org
  • Python Library Websites
  • W3 Schools
  • Pythonbooks
  • The Python Papers
  • Python for Everybody
  • KDNuggets
  • Geeks for Geeks
  • Real Python
  • The Zen of Python
  • Index
  • About the Author
  • Connect with Dummies
  • End User License Agreement