Introductory Statistics, 10th
Edition
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Author (s): Prem S. Mann
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This comprehensive test bank for Introductory Statistics, 10th Edition by Prem S. Mann, includes full coverage of Chapters 1 through 15, offering a robust set of assessment tools aligned with the latest edition of the textbook. Each chapter is supported with carefully crafted multiple-choice, true/false, and applied scenario-based questions that reflect the learning objectives, formulas, and statistical techniques emphasized throughout the book. The test bank begins with Chapter 1: Introduction, which lays the foundation for statistical thinking and the role of statistics in decision-making. Chapter 2: Organizing and Graphing Data explores data presentation using tables and graphical methods. In Chapter 3: Numerical Descriptive Measures, students analyze data using central tendency, variability, and position metrics. Chapter 4: Probability introduces the rules and interpretations of probability essential for understanding statistical inference. Chapter 5: Discrete Random Variables and Their Probability Distributions and Chapter 6: Continuous Random Variables and the Normal Distribution delve into the behavior of different types of random variables and their applications. The concepts of inference begin in Chapter 7: Sampling Distributions, followed by Chapter 8: Estimation of the Mean and Proportion, which introduces point and interval estimates. Hypothesis testing is covered in Chapter 9: Hypothesis Tests About the Mean and Proportion, and extended in Chapter 10: Estimation and Hypothesis Testing: Two Populations, dealing with comparative analyses between groups. Chapter 11: Chi-Square Tests introduces tests for independence and goodness-of-fit. Chapter 12: Analysis of Variance (ANOVA) provides tools for comparing more than two means, while Chapter 13: Simple Linear Regression and Chapter 14: Multiple Regression equip students with modeling and prediction tools using one or more predictors. The test bank concludes with Chapter 15: Nonparametric Methods, offering distribution-free techniques appropriate for ordinal data and small samples.
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