Loose Leaf Elementary Statistics with Formula Card
2nd EditionISBN10: 1259345300
ISBN13: 9781259345302
Copyright: 2016
Instructors: choose ebook for fast access or receive a print copy.
Still Have Questions? Contact your Rep s
With the McGraw Hill eBook, students can access their digital textbook on the web or go offline via the ReadAnywhere app for phones or tablets.
McGraw Hill eBook Courses Include:
- Offline reading – study anytime, anywhere
- One interface for all McGraw Hill eBooks
- Highlighting and note-taking
- Syncs across platforms, always up-to-date
- Available for Android and iOS
Rent Monthly
Purchase Options
Students, we’re committed to providing you with high-value course solutions backed by great service and a team that cares about your success. See tabs below to explore options and pricing. Don't forget, we accept financial aid and scholarship funds in the form of credit or debit cards.
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.
Accessibility Rubric
Creating accessible products is a priority for McGraw-Hill. We have put in place processes to make accessibility and meeting the WCAG AA guidelines part of our day-to-day development efforts and product roadmaps.
Please review our accessibility information for this specific product.
In future editions, this rubric will be reformatted to increase accessibility and usability.
McGraw-Hill sites may contain links to websites owned and operated by third parties. These links are provided as supplementary materials, and for learners’ information and convenience only. McGraw-Hill has no control over and is not responsible for the content or accessibility of any linked website.
For further information on McGraw‐Hill and Accessibility, please visit our accessibility page or contact us at accessibility@mheducation.com
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.
Accessibility Rubric
Creating accessible products is a priority for McGraw-Hill. We have put in place processes to make accessibility and meeting the WCAG AA guidelines part of our day-to-day development efforts and product roadmaps.
Please review our accessibility information for this specific product.
In future editions, this rubric will be reformatted to increase accessibility and usability.
McGraw-Hill sites may contain links to websites owned and operated by third parties. These links are provided as supplementary materials, and for learners’ information and convenience only. McGraw-Hill has no control over and is not responsible for the content or accessibility of any linked website.
For further information on McGraw‐Hill and Accessibility, please visit our accessibility page or contact us at accessibility@mheducation.com
Shipping Options
- Standard
- Next day air
- 2nd day air
- 3rd day air
Rent Now
You will be taken to our partner Chegg.com to complete your transaction.
After completing your transaction, you can access your course using the section url supplied by your instructor.