Elementary Statistics
International Edition
5th Edition
Published on 18. March 2004
Book
Paperback/Softback
768 pages
978-0-321-26344-5 (ISBN)
Description
Elementary Statistics is intended for a one-semester introductory statistics course. The purpose of this book is to help students grasp basic statistical concepts and techniques, and to present real-life opportunities for applying them. With advances in technology and new insights into the practice of teaching statistics, the fifth edition can now easily fit the organization and pace of various course syllabi and technologies in use. This book offers a flexible organization of content and has a more diversified emphasis on using technology such as Minitab, the TI-83 Plus graphing calculator, Excel, and the Internet to investigate statistical problems.
More details
Edition
5th edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 252 mm
Width: 216 mm
Thickness: 25 mm
Weight
1464 gr
ISBN-13
978-0-321-26344-5 (9780321263445)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Neil A. Weiss
Elementary Statistics
Book
09/2004
6th Edition
Pearson
€63.13
Article exhausted; check for reprint
Content
I. INTRODUCTION.
1. The Nature of Statistics.
Case Study: Top Films of All Time.
Two Kinds of Statistics.
The Technology Center.
Simple Random Sampling.
Other Sampling Designs.
Experimental Designs.
II. DESCRIPTIVE STATISTICS.
2. Organizing Data.
Case Study: Preventing Infant Mortality.
Variables and Data.
Grouping Data.
Graphs and Charts.
Stem-and-Leaf Diagrams.
Distribution Shapes; Symmetry and Skewness.
Misleading Graphs.
3. Descriptive Measures.
Case Study: New York Yankees Y2K Salaries.
Measures of Center.
The Sample Mean.
Measures of Variation; the Sample Standard Deviation.
The Five-Number Summary; Boxplots.
Descriptive Measures for Populations; Use of Samples.
4. Descriptive Methods in Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
Linear Equations With One Independent Variable.
The Regression Equation.
The Coefficient of Determination.
Linear Correlation.
III. PROBABILITY, RANDOM VARIABLES, AND SAMPLING DISTRIBUTIONS.
5. Probability and Random Variables.
Case Study: The Powerball.
Probability Basics.
Events.
Some Rules of Probability.
*Discrete Random Variables and Probability Distributions.
*The Mean and Standard Deviation of a Discrete Random Variable.
*The Binomial Distribution.
6. The Normal Distribution.
Case Study: Chest Sizes of Scottish Militiamen.
Introducing Normally Distributed Variables.
Areas Under the Standard Normal Curve.
Working With Normally Distributed Variables.
Assessing Normality; Normal Probability Plots.
7. The Sampling Distribution of the Sample Mean.
Case Study: The Chesapeake and Ohio Freight Study.
Sampling Error; the Need for Sampling Distributions.
The Mean and Standard Deviation of x.
The Sampling Distribution of the Sample Mean.
IV. INFERENTIAL STATISTICS.
8. Confidence Intervals for One Population Mean.
Case Study: The Chips Ahoy! 1,000 Chips Challenge.
Estimating a Population Mean.
Confidence Intervals for One Population Mean When s Is Known.
Margin of Error.
Confidence Intervals for One Population Mean When s Is Unknown.
9. Hypothesis Tests for One Population Mean.
Case Study: Sex and Sense of Direction.
The Nature of Hypothesis Testing.
Terms, Errors, and Hypotheses.
Hypothesis Tests for One Population Mean When s Is Known.
P-Values.
Hypothesis Tests for One Population Mean When s is Unknown.
10. Inferences for Two Population Means.
Case Study: Breast Milk and IQ.
The Sampling Distribution of the Difference Between Two Sample Means for Independent Samples.
Inferences for Two Population Means Using Independent Samples: Standard Deviations Assumed Equal.
Inferences for Two Population Means Using Independent Samples: Standard Deviations Not Assumed Equal.
Inferences for Two Population Means Using Paired Samples.
11. Inferences for Population Proportions.
Case Study: Double-Dipping ATM Fees.
Confidence Intervals for One Population Proportion.
Hypothesis Tests for One Population Proportion.
Inferences for Two Population Proportions Using Independent Samples.
12. Chi-Square Procedures.
Case Study: Road Rage.
The Chi-Square Distribution.
Chi-Square Goodness-Of-Fit Test.
Contingency Tables; Association.
Chi-Square Independence Test.
13. Analysis of Variance (ANOVA).
Case Study: Heavy Drinking Among College Students.
The F-Distribution.
One-Way ANOVA: The Logic.
One-Way ANOVA: The Procedure.
14. Inferential Methods In Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
The Regression Model; Analysis of Residuals.
Inferences for the Slope of the Population Regression Line.
Estimation and Prediction.
Inferences in Correlation.
APPENDIXES.
Appendix A. Statistical Tables.
Appendix B. Answers To Selected Exercises.
Index.
1. The Nature of Statistics.
Case Study: Top Films of All Time.
Two Kinds of Statistics.
The Technology Center.
Simple Random Sampling.
Other Sampling Designs.
Experimental Designs.
II. DESCRIPTIVE STATISTICS.
2. Organizing Data.
Case Study: Preventing Infant Mortality.
Variables and Data.
Grouping Data.
Graphs and Charts.
Stem-and-Leaf Diagrams.
Distribution Shapes; Symmetry and Skewness.
Misleading Graphs.
3. Descriptive Measures.
Case Study: New York Yankees Y2K Salaries.
Measures of Center.
The Sample Mean.
Measures of Variation; the Sample Standard Deviation.
The Five-Number Summary; Boxplots.
Descriptive Measures for Populations; Use of Samples.
4. Descriptive Methods in Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
Linear Equations With One Independent Variable.
The Regression Equation.
The Coefficient of Determination.
Linear Correlation.
III. PROBABILITY, RANDOM VARIABLES, AND SAMPLING DISTRIBUTIONS.
5. Probability and Random Variables.
Case Study: The Powerball.
Probability Basics.
Events.
Some Rules of Probability.
*Discrete Random Variables and Probability Distributions.
*The Mean and Standard Deviation of a Discrete Random Variable.
*The Binomial Distribution.
6. The Normal Distribution.
Case Study: Chest Sizes of Scottish Militiamen.
Introducing Normally Distributed Variables.
Areas Under the Standard Normal Curve.
Working With Normally Distributed Variables.
Assessing Normality; Normal Probability Plots.
7. The Sampling Distribution of the Sample Mean.
Case Study: The Chesapeake and Ohio Freight Study.
Sampling Error; the Need for Sampling Distributions.
The Mean and Standard Deviation of x.
The Sampling Distribution of the Sample Mean.
IV. INFERENTIAL STATISTICS.
8. Confidence Intervals for One Population Mean.
Case Study: The Chips Ahoy! 1,000 Chips Challenge.
Estimating a Population Mean.
Confidence Intervals for One Population Mean When s Is Known.
Margin of Error.
Confidence Intervals for One Population Mean When s Is Unknown.
9. Hypothesis Tests for One Population Mean.
Case Study: Sex and Sense of Direction.
The Nature of Hypothesis Testing.
Terms, Errors, and Hypotheses.
Hypothesis Tests for One Population Mean When s Is Known.
P-Values.
Hypothesis Tests for One Population Mean When s is Unknown.
10. Inferences for Two Population Means.
Case Study: Breast Milk and IQ.
The Sampling Distribution of the Difference Between Two Sample Means for Independent Samples.
Inferences for Two Population Means Using Independent Samples: Standard Deviations Assumed Equal.
Inferences for Two Population Means Using Independent Samples: Standard Deviations Not Assumed Equal.
Inferences for Two Population Means Using Paired Samples.
11. Inferences for Population Proportions.
Case Study: Double-Dipping ATM Fees.
Confidence Intervals for One Population Proportion.
Hypothesis Tests for One Population Proportion.
Inferences for Two Population Proportions Using Independent Samples.
12. Chi-Square Procedures.
Case Study: Road Rage.
The Chi-Square Distribution.
Chi-Square Goodness-Of-Fit Test.
Contingency Tables; Association.
Chi-Square Independence Test.
13. Analysis of Variance (ANOVA).
Case Study: Heavy Drinking Among College Students.
The F-Distribution.
One-Way ANOVA: The Logic.
One-Way ANOVA: The Procedure.
14. Inferential Methods In Regression and Correlation.
Case Study: Fat Consumption and Prostate Cancer.
The Regression Model; Analysis of Residuals.
Inferences for the Slope of the Population Regression Line.
Estimation and Prediction.
Inferences in Correlation.
APPENDIXES.
Appendix A. Statistical Tables.
Appendix B. Answers To Selected Exercises.
Index.