Think Stats: Exploratory Data Analysis. 3 Ed

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Copyright....4 Table of Contents....5 Preface....9 What’s New?....11 Using the Code....12 Conventions Used in This Book....13 O’Reilly Online Learning....14 How to Contact Us....14 Acknowledgments....14 Chapter 1. Exploratory Data Analysis....15 Evidence....15 The National Survey of Family Growth....17 Reading the Data....18 Validation....21 Transformation....24 Summary Statistics....25 Interpretation....26 Glossary....27 Exercises....29 Exercise 1.1....29 Exercise 1.2....29 Exercise 1.3....29 Chapter 2. Distributions....31 Frequency Tables....31 NSFG Distributions....33 Outliers....37 First Babies....38 Effect Size....40 Reporting Results....42 Glossary....43 Exercises....43 Exercise 2.1....44 Exercise 2.2....44 Exercise 2.3....44 Chapter 3. Probability Mass Functions....45 PMFs....45 Summarizing a PMF....48 The Class Size Paradox....50 NSFG Data....53 Other Visualizations....54 Glossary....55 Exercises....56 Exercise 3.1....56 Exercise 3.2....56 Exercise 3.3....57 Chapter 4. Cumulative Distribution Functions....59 Percentiles and Percentile Ranks....59 CDFs....62 Comparing CDFs....66 Percentile-Based Statistics....68 Random Numbers....72 Glossary....74 Exercises....75 Exercise 4.1....75 Exercise 4.2....75 Exercise 4.3....76 Exercise 4.4....76 Exercise 4.5....76 Chapter 5. Modeling Distributions....77 The Binomial Distribution....77 The Poisson Distribution....82 The Exponential Distribution....86 The Normal Distribution....90 The Lognormal Distribution....93 Why Model?....97 Glossary....98 Exercises....98 Exercise 5.1....98 Exercise 5.2....99 Exercise 5.3....99 Chapter 6. Probability Density Functions....101 Comparing Distributions....101 Probability Density....104 The Exponential PDF....107 Comparing PMFs and PDFs....109 Kernel Density Estimation....111 The Distribution Framework....115 Glossary....120 Exercises....121 Exercise 6.1....121 Exercise 6.2....121 Chapter 7. Relationships Between Variables....123 Scatter Plots....123 Decile Plots....128 Correlation....130 Strength of Correlation....134 Rank Correlation....136 Correlation and Causation....139 Glossary....140 Exercises....141 Exercise 7.1....141 Exercise 7.2....141 Exercise 7.3....142 Exercise 7.4....142 Exercise 7.5....143 Chapter 8. Estimation....145 Weighing Penguins....145 Robustness....149 Estimating Variance....151 Sampling Distributions....152 Standard Error....155 Confidence Intervals....156 Sources of Error....157 Glossary....157 Exercises....159 Exercise 8.1....159 Exercise 8.2....159 Exercise 8.3....159 Exercise 8.4....160 Exercise 8.5....160 Exercise 8.6....161 Chapter 9. Hypothesis Testing....163 Flipping Coins....163 Testing a Difference in Means....166 Other Test Statistics....169 Testing a Correlation....170 Testing Proportions....172 Glossary....176 Exercises....176 Exercise 9.1....176 Exercise 9.2....177 Chapter 10. Least Squares....179 Least Squares Fit....179 Coefficient of Determination....183 Minimizing MSE....185 Estimation....187 Visualizing Uncertainty....189 Transformation....191 Glossary....196 Exercises....196 Exercise 10.1....196 Exercise 10.2....197 Exercise 10.3....197 Chapter 11. Multiple Regression....199 StatsModels....199 On to Multiple Regression....203 Control Variables....205 Nonlinear Relationships....209 Logistic Regression....212 Glossary....216 Exercises....217 Exercise 11.1....217 Exercise 11.2....217 Exercise 11.3....218 Exercise 11.4....218 Chapter 12. Time Series Analysis....219 Electricity....219 Decomposition....220 Prediction....227 Multiplicative Model....231 Autoregression....236 Moving Average....238 Retrodiction with Autoregression....240 ARIMA....242 Prediction with ARIMA....244 Glossary....245 Exercises....246 Exercise 12.1....246 Exercise 12.2....248 Exercise 12.3....249 Chapter 13. Survival Analysis....251 Survival Functions....251 Hazard Function....253 Marriage Data....255 Weighted Bootstrap....258 Estimating Hazard Functions....260 Estimating Survival Functions....263 Lifelines....265 Confidence Intervals....266 Expected Remaining Lifetime....268 Glossary....272 Exercises....272 Exercise 13.1....272 Exercise 13.2....273 Chapter 14. Analytic Methods....275 Normal Probability Plots....275 Normal Distributions....280 Distribution of Sample Means....284 Distribution of Differences....286 Central Limit Theorem....288 The Limits of the Central Limit Theorem....290 Applying the CLT....292 Correlation Test....295 Chi-squared Test....299 Computation and Analysis....302 Glossary....303 Exercises....303 Exercise 14.1....303 Exercise 14.2....304 Exercise 14.3....304 Exercise 14.4....304 Index....307 About the Author....323 Colophon....323
Описание
Коротко и по делу о том, что важно знать про data.
This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. If you know how to program, you have the skills to turn data into knowledge. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis—from wrangling data and generating statistics to identifying patterns and testing hypotheses.
You'll explore distributions, relationships between variables, visualization, and many other concepts. Whether you're a data scientist, software engineer, or data enthusiast, you'll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place.
Analyze data distributions and visualize patterns using Python librariesImprove predictions and insights with regression modelsDive into specialized topics like time series analysis and survival analysisIntegrate statistical techniques and tools for validation, inference, and moreCommunicate findings with effective data visualizationTroubleshoot common data analysis challengesBoost reproducibility and collaboration in data analysis projects with interactive notebooks
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автор — Downey Allen, издательство O’Reilly Media, Inc., год выпуска 2025, 324 страниц.
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If you know how to program, you have the skills to turn data into knowledge.