# Loose Leaf Version for Elementary Statistics

2^{nd}Edition

ISBN10: 1259292134

ISBN13: 9781259292132

Copyright: 2016

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The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

### Program Details

## Chapter 1: Basic Ideas

### 1.1 Sampling

### 1.2 Types of Data

### 1.3 Design of Experiments

### 1.4 Bias in Study

### * Chapter 1 Summary *

### * Chapter Quiz *

### * Chapter 1 Review Exercises *

### * Case Study *

## Chapter 2: Graphical Summaries of Data

### 2.1 Graphical Summaries for Qualitative Data

### 2.2 Frequency Distributions and Their Graphs

### 2.3 More Graphs for Quantitative Data

### 2.4 Graphs Can Be Misleading

### * Chapter 2 Summary *

### * Chapter Quiz *

### * Chapter 2 Review Exercises *

### * Case Study *

## Chapter 3: Numerical Summaries of Data

### 3.1 Measures of Center Data

### 3.2 Measures of Spread

### 3.3 Measures of Position

### * Chapter 3 Summary *

### * Chapter Quiz *

### * Chapter 3 Review Exercises *

### * Case Study *

## Chapter 4: Summarizing Bivariate Data

### 4.1 Correlation

### 4.2 The Least-Squares Regression Line

### 4.3 Features and Limitations of the Least-Squares Regression Line

### * Chapter 4 Summary *

### * Chapter Quiz *

### * Chapter 4 Review Exercises *

### * Case Study *

## Chapter 5: Probability

### 5.1 Basic Ideas

### 5.2 The Addition Rule and the Rule of Compliments

### 5.3 Conditional Probability and the Multiplication Rule

### 5.4 Counting

### * Chapter 5 Summary *

### * Chapter Quiz *

### * Chapter 5 Review Exercises *

### * Case Study *

## Chapter 6: Discrete Probability Distributions

### 6.1 Random Variables

### 6.2 The Binomial Distribution

### 6.3 The Poisson Distribution

### * Chapter 6 Summary *

### * Chapter Quiz *

### * Chapter 6 Review Exercises *

### * Case Study *

## Chapter 7: The Normal Distribution

### 7.1 The Standard Normal Curve

### 7.2 Applications of the Normal Distribution

### 7.3 Sampling Distributions and the Central Limit Theorem

### 7.4 The Central Limit Theorem for Proportions

### 7.5 The Normal Approximation to the Binomial Distribution

### 7.6 Assessing Normality

### * Chapter 7 Summary *

### * Chapter Quiz *

### * Chapter 7 Review Exercises *

### * Case Study *

## Chapter 8: Confidence Intervals

### 8.1 Confidence Intervals for a Population Mean, Standard Deviation Known

### 8.2 Confidence Intervals for a Population Mean, Standard Deviation Unknown

### 8.3 Confidence Intervals for a Population Proportion

### 8.4 The Central Limit Theorem for Proportions

### 8.5 Determining Which Method to Use

### * Chapter 8 Summary *

### * Chapter Quiz *

### * Chapter 8 Review Exercises *

### * Case Study *

## Chapter 9: Hypothesis Testing

### 9.1 Basic Principles of Hypothesis Testing

### 9.2 Hypothesis Tests for a Population Mean, Standard Deviation Known

### 9.3 Hypothesis Tests for a Population Mean, Standard Deviation Unknown

### 9.4 Hypothesis Tests for Proportions

### 9.5 Hypothesis Tests for a Standard Deviation

### 9.6 Determining Which Method to Use

### 9.7 Power

### * Chapter 9 Summary *

### * Chapter Quiz *

### * Chapter 9 Review Exercises *

### * Case Study *

## Chapter 10: Two-Sample Confidence Intervals

### 10.1 Confidence Intervals for the Difference Between Two Means: Independent Samples

### 10.2 Confidence Intervals for the Difference Between Two Proportions

### 10.3 Confidence Intervals for the Difference Between Two Means: Paired Samples

### * Chapter 10 Summary *

### * Chapter Quiz *

### * Chapter 10 Review Exercises *

### * Case Study *

## Chapter 11: Two-Sample Hypothesis Tests

### 11.1 Hypothesis Tests for the Difference Between Two Means: Independent Samples

### 11.2 Hypothesis Tests for the Difference Between Two Proportions

### 11.3 Hypothesis Tests for the Difference Between Two Means: Paired Samples

### 11.4 Hypothesis Tests for Two Population Standard Deviations

### 11.5 The Multiple Testing Problem

### * Chapter 11 Summary *

### * Chapter Quiz *

### * Chapter 11 Review Exercises *

### * Case Study *

## Chapter 12: Tests with Qualitative Data

### 12.1 Testing Goodness-of-Fit

### 12.2 Tests for Independence and Homogeneity

### * Chapter 12 Summary *

### * Chapter Quiz *

### * Chapter 12 Review Exercises *

### * Case Study *

## Chapter 13: Inference in Linear Models

### 13.1 Inference on the Slope of the Regression Line

### 13.2 Inference About the Response

### * Chapter 13 Summary *

### * Chapter Quiz *

### * Chapter 13 Review Exercises *

### * Case Study *

## Chapter 14: Analysis of Variance

### 14.1 One-way Analysis of Variance

### 14.2 Two-way Analysis of Variance

### * Chapter 14 Summary *

### * Chapter Quiz *

### * Chapter 14 Review Exercises *

### * Case Study *

# About the Author

**William Navidi**

William Navidi received a B.A. in mathematics from New College, an M.A in mathematics from Michigan State University, and a Ph.D. in statistics from the University of California at Berkeley. Dr. Navidi is a professor of applied mathematics and statistics at the Colorado School of Mines in Golden, Colorado. He began his teaching career at the County College of Morris in Dover, New Jersey. He has taught mathematics and statistics at all levels, from developmental through the graduate level. Dr. Navidi has written two engineering statistics textbooks for McGraw-Hill and has authored more than 50 research papers, both in statistical theory and in a wide variety of applications, including computer networks, epidemiology, molecular biology, chemical engineering, and geophysics.

**Barry Monk**

Barry Monk received a B.S. in mathematical statistics, an M.A. in mathematics specializing in optimization and statistics, and a Ph.D. in applied mathematics, all from the University of Alabama. Dr. Monk is a professor of mathematics at Middle Georgia State University in Macon, Georgia, where he has been employed since 2001. Dr. Monk was asked to serve as program coordinator of mathematics after only two years at Macon State College; he led the development of the bachelor's degree in mathematics program and the organization of the Southeastern Scholarship Conference on E-Learning for more than 10 years. He has been teaching introductory statistics since 1992 in the classroom and online.

## Chapter 1: Basic Ideas

### 1.1 Sampling

### 1.2 Types of Data

### 1.3 Design of Experiments

### 1.4 Bias in Study

### * Chapter 1 Summary *

### * Chapter Quiz *

### * Chapter 1 Review Exercises *

### * Case Study *

## Chapter 2: Graphical Summaries of Data

### 2.1 Graphical Summaries for Qualitative Data

### 2.2 Frequency Distributions and Their Graphs

### 2.3 More Graphs for Quantitative Data

### 2.4 Graphs Can Be Misleading

### * Chapter 2 Summary *

### * Chapter Quiz *

### * Chapter 2 Review Exercises *

### * Case Study *

## Chapter 3: Numerical Summaries of Data

### 3.1 Measures of Center Data

### 3.2 Measures of Spread

### 3.3 Measures of Position

### * Chapter 3 Summary *

### * Chapter Quiz *

### * Chapter 3 Review Exercises *

### * Case Study *

## Chapter 4: Summarizing Bivariate Data

### 4.1 Correlation

### 4.2 The Least-Squares Regression Line

### 4.3 Features and Limitations of the Least-Squares Regression Line

### * Chapter 4 Summary *

### * Chapter Quiz *

### * Chapter 4 Review Exercises *

### * Case Study *

## Chapter 5: Probability

### 5.1 Basic Ideas

### 5.2 The Addition Rule and the Rule of Compliments

### 5.3 Conditional Probability and the Multiplication Rule

### 5.4 Counting

### * Chapter 5 Summary *

### * Chapter Quiz *

### * Chapter 5 Review Exercises *

### * Case Study *

## Chapter 6: Discrete Probability Distributions

### 6.1 Random Variables

### 6.2 The Binomial Distribution

### 6.3 The Poisson Distribution

### * Chapter 6 Summary *

### * Chapter Quiz *

### * Chapter 6 Review Exercises *

### * Case Study *

## Chapter 7: The Normal Distribution

### 7.1 The Standard Normal Curve

### 7.2 Applications of the Normal Distribution

### 7.3 Sampling Distributions and the Central Limit Theorem

### 7.4 The Central Limit Theorem for Proportions

### 7.5 The Normal Approximation to the Binomial Distribution

### 7.6 Assessing Normality

### * Chapter 7 Summary *

### * Chapter Quiz *

### * Chapter 7 Review Exercises *

### * Case Study *

## Chapter 8: Confidence Intervals

### 8.1 Confidence Intervals for a Population Mean, Standard Deviation Known

### 8.2 Confidence Intervals for a Population Mean, Standard Deviation Unknown

### 8.3 Confidence Intervals for a Population Proportion

### 8.4 The Central Limit Theorem for Proportions

### 8.5 Determining Which Method to Use

### * Chapter 8 Summary *

### * Chapter Quiz *

### * Chapter 8 Review Exercises *

### * Case Study *

## Chapter 9: Hypothesis Testing

### 9.1 Basic Principles of Hypothesis Testing

### 9.2 Hypothesis Tests for a Population Mean, Standard Deviation Known

### 9.3 Hypothesis Tests for a Population Mean, Standard Deviation Unknown

### 9.4 Hypothesis Tests for Proportions

### 9.5 Hypothesis Tests for a Standard Deviation

### 9.6 Determining Which Method to Use

### 9.7 Power

### * Chapter 9 Summary *

### * Chapter Quiz *

### * Chapter 9 Review Exercises *

### * Case Study *

## Chapter 10: Two-Sample Confidence Intervals

### 10.1 Confidence Intervals for the Difference Between Two Means: Independent Samples

### 10.2 Confidence Intervals for the Difference Between Two Proportions

### 10.3 Confidence Intervals for the Difference Between Two Means: Paired Samples

### * Chapter 10 Summary *

### * Chapter Quiz *

### * Chapter 10 Review Exercises *

### * Case Study *

## Chapter 11: Two-Sample Hypothesis Tests

### 11.1 Hypothesis Tests for the Difference Between Two Means: Independent Samples

### 11.2 Hypothesis Tests for the Difference Between Two Proportions

### 11.3 Hypothesis Tests for the Difference Between Two Means: Paired Samples

### 11.4 Hypothesis Tests for Two Population Standard Deviations

### 11.5 The Multiple Testing Problem

### * Chapter 11 Summary *

### * Chapter Quiz *

### * Chapter 11 Review Exercises *

### * Case Study *

## Chapter 12: Tests with Qualitative Data

### 12.1 Testing Goodness-of-Fit

### 12.2 Tests for Independence and Homogeneity

### * Chapter 12 Summary *

### * Chapter Quiz *

### * Chapter 12 Review Exercises *

### * Case Study *

## Chapter 13: Inference in Linear Models

### 13.1 Inference on the Slope of the Regression Line

### 13.2 Inference About the Response

### * Chapter 13 Summary *

### * Chapter Quiz *

### * Chapter 13 Review Exercises *

### * Case Study *

## Chapter 14: Analysis of Variance

### 14.1 One-way Analysis of Variance

### 14.2 Two-way Analysis of Variance

### * Chapter 14 Summary *

### * Chapter Quiz *

### * Chapter 14 Review Exercises *

### * Case Study *

# About the Author

**William Navidi**

William Navidi received a B.A. in mathematics from New College, an M.A in mathematics from Michigan State University, and a Ph.D. in statistics from the University of California at Berkeley. Dr. Navidi is a professor of applied mathematics and statistics at the Colorado School of Mines in Golden, Colorado. He began his teaching career at the County College of Morris in Dover, New Jersey. He has taught mathematics and statistics at all levels, from developmental through the graduate level. Dr. Navidi has written two engineering statistics textbooks for McGraw-Hill and has authored more than 50 research papers, both in statistical theory and in a wide variety of applications, including computer networks, epidemiology, molecular biology, chemical engineering, and geophysics.

**Barry Monk**

Barry Monk received a B.S. in mathematical statistics, an M.A. in mathematics specializing in optimization and statistics, and a Ph.D. in applied mathematics, all from the University of Alabama. Dr. Monk is a professor of mathematics at Middle Georgia State University in Macon, Georgia, where he has been employed since 2001. Dr. Monk was asked to serve as program coordinator of mathematics after only two years at Macon State College; he led the development of the bachelor's degree in mathematics program and the organization of the Southeastern Scholarship Conference on E-Learning for more than 10 years. He has been teaching introductory statistics since 1992 in the classroom and online.

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