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📘 Statistics: The Art and Science of Learning from Data – 5th Edition (Agresti) – Test Bank Overview

Unlock the ultimate test bank for Statistics: The Art and Science of Learning from Data, 5th Edition by Alan Agresti, Christine A. Franklin, Bernhard Klingenberg (ISBN-13: 9780136468769). Perfect for students, instructors, and data enthusiasts, this comprehensive guide covers all major topics in statistics from data collection to advanced regression.

🟢 I: Gathering and Exploring Data

1️⃣ Statistics: The Art and Science of Learning From Data

• 1.1 Using Data to Answer Statistical Questions ✅

• 1.2 Sample Versus Population 📊

• 1.3 Organizing Data, Statistical Software, and Data Science 💻

• Chapter Summary & Exercises 📝

2️⃣ Exploring Data With Graphs and Numerical Summaries

• 2.1 Different Types of Data 🔢

• 2.2 Graphical Summaries of Data 📈

• 2.3 Measuring the Center of Quantitative Data 🎯

• 2.4 Measuring the Variability of Quantitative Data 📉

• 2.5 Measures of Position & Variability 📏

• 2.6 Linear Transformations & Standardizing ⚡

• 2.7 Avoiding Misuse of Graphical Summaries 🚫

• Chapter Summary & Exercises 📝

3️⃣ Exploring Relationships Between Two Variables

• 3.1 Association Between Two Categorical Variables 🟠

• 3.2 Relationship Between Two Quantitative Variables 🔵

• 3.3 Linear Regression: Predicting Outcomes 📐

• 3.4 Cautions in Analyzing Associations ⚠️

• Chapter Summary & Exercises 📝

4️⃣ Gathering Data

• 4.1 Experimental vs Observational Studies 🔬

• 4.2 Good and Poor Sampling Techniques 🧩

• 4.3 Good and Poor Experimental Methods 🧪

• 4.4 Other Experimental & Nonexperimental Methods 🔄

• Chapter Summary & Exercises 📝

🟡 II: Probability, Probability Distributions, and Sampling Distributions

5️⃣ Probability in Our Daily Lives

• 5.1 How Probability Quantifies Randomness 🎲

• 5.2 Finding Probabilities ➕

• 5.3 Conditional Probability 🔗

• 5.4 Probability Rules Applications 📏

• Chapter Summary & Exercises 📝

6️⃣ Random Variables & Probability Distributions

• 6.1 Summarizing Outcomes and Probabilities 📊

• 6.2 Probabilities for Bell-Shaped Distributions 🔔

• 6.3 Probabilities for Binary Outcomes 0️⃣1️⃣

• Chapter Summary & Exercises 📝

7️⃣ Sampling Distributions

• 7.1 How Sample Proportions Vary 📈

• 7.2 How Sample Means Vary 📉

• 7.3 Using Bootstrap for Sampling Distributions 🥾

• Chapter Summary & Exercises 📝

🔵 III: Inferential Statistics

8️⃣ Statistical Inference: Confidence Intervals

• 8.1 Point & Interval Estimates 🎯

• 8.2 Confidence Intervals for Proportions 📊

• 8.3 Confidence Intervals for Means 📈

• 8.4 Bootstrap Confidence Intervals 🥾

• Chapter Summary & Exercises 📝

9️⃣ Statistical Inference: Significance Tests

• 9.1 Steps for Significance Tests ✅

• 9.2 Tests About Proportions 📊

• 9.3 Tests About a Mean 📈

• 9.4 Decisions & Type Errors ⚠️

• 9.5 Limitations of Significance Tests 🚫

• 9.6 Type II Error Likelihood ❌

• Chapter Summary & Exercises 📝

🔹 10 Comparing Two Groups

• 10.1 Comparing Two Proportions 📊

• 10.2 Comparing Two Means 📈

• 10.3 Bootstrap & Permutation Resampling 🔄

• 10.4 Dependent Sample Analysis 🔗

• 10.5 Adjusting for Other Variables ⚖️

• Chapter Summary & Exercises 📝

🟣 IV: Analyzing Associations & Advanced Methods

11️⃣ Association Between Categorical Variables

• 11.1 Independence & Dependence 🔗

• 11.2 Testing for Independence ✅

• 11.3 Strength of Association 💪

• 11.4 Residuals for Patterns 🧩

• 11.5 Fisher’s Exact & Permutation Tests 🎯

• Chapter Summary & Exercises 📝

12️⃣ Regression Analysis: Quantitative Variables

• 12.1 Modeling Relationships 📐

• 12.2 Inference About Parameters 📊

• 12.3 Strength of Association 💪

• 12.4 Data Variation Around Regression Line 📉

• 12.5 Exponential Regression & Nonlinearity 🔺

• Chapter Summary & Exercises 📝

13️⃣ Multiple Regression

• 13.1 Using Multiple Predictors 🔗

• 13.2 Correlation & R² in Multiple Regression 📏

• 13.3 Making Inferences ✅

• 13.4 Residual Plots for Model Checking 🧩

• 13.5 Regression & Categorical Predictors 🟠

• 13.6 Modeling Categorical Response 📊

• Chapter Summary & Exercises 📝

14️⃣ Comparing Groups: ANOVA Methods

• 14.1 One-Way ANOVA: Several Means 📈

• 14.2 Estimating Differences for a Single Factor 📊

• 14.3 Two-Way ANOVA 🔄

• Chapter Summary & Exercises 📝

15️⃣ Nonparametric Statistics

• 15.1 Comparing Two Groups by Ranking 🏆

• 15.2 Nonparametric Methods for Several Groups & Matched Pairs 🔢

• Chapter Summary & Exercises 📝

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