Introductory Statistics
6th Edition
Published on 24. August 2001
Book
Hardback
984 pages
978-0-201-71059-5 (ISBN)
Description
Weiss Introductory Statistics is intended for a one- or two-semester introductory statistics course. Students learn the core statistical concepts in an applied setting, and can access more advanced topics (multiple regression, ANOVA, and Experimental Design) through chapters available on the WeissStat CD. With advances in technology and new insights into the practice of teaching statistics, the sixth edition can now easily fit the organization and pace of various course syllabi and technologies in use. The 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
6th edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 262 mm
Width: 226 mm
Thickness: 36 mm
Weight
2099 gr
ISBN-13
978-0-201-71059-5 (9780201710595)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Book
06/2004
7th Edition
Pearson
€96.78
Article exhausted; check for reprint
Previous edition
Weiss
Introductory Statistics
Book
08/1999
5th Edition
Addison Wesley
€44.28
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.
III. PROBABILITY, RANDOM VARIABLES, AND SAMPLING DISTRIBUTIONS.
4. Probability Concepts.
Case Study: The Powerball.
Probability Basics.
Events.
Some Rules of Probability.
*Contingency Tables; Joint and Marginal Probabilities.
*Conditional Probability.
*The Multiplication Rule; Independence.
*Bayes's Rule.
*Counting Rules.
5. Discrete Random Variables.
Case Study: Aces Wild on the Sixth at Oak Hill.
*Discrete Random Variables and Probability Distributions.
*The Mean and Standard Deviation of a Discrete Random Variable.
*The Binomial Distribution.
*The Poisson 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.
*Normal Approximation to the Binomial Distribution .
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.
*Type II Error Probabilities; Power.
P-Values.
Hypothesis Tests for One Population Mean When s Is Unknown.
*The Wilcoxon Signed-Rank Test.
*Which Procedure Should Be Used?
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.
*The Mann-Whitney Test.
Inferences for Two Population Means Using Paired Samples.
*The Paired Wilcoxon Signed-Rank Test.
*Which Procedure Should Be Used?
11. Inferences for Population Standard Deviations.
Case Study: Speaker Woofer Driver Manufacturing.
*Inferences for One Population Standard Deviation.
*Inferences for Two Population Standard Deviations Using Independent Samples.
12. 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.
13. Chi-Square Procedures.
Case Study: Road Rage.
The Chi-Square Distribution.
Chi-Square Goodness-Of-Fit Test.
Contingency Tables; Association.
Chi-Square Independence Test.
V. REGRESSION, CORRELATION, AND ANOVA.
14. 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.
15. 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.
*Testing for Normality.
16. Analysis of Variance (Anova).
Case Study: Heavy Drinking Among College Students.
The F-Distribution.
One-Way ANOVA: The Logic.
One-Way ANOVA: The Procedure.
*Multiple Comparisons.
*The Kruskal-Wallis Test.
VI. MULTIPLE REGRESSION AND MODEL BUILDING; EXPERIMENTAL DESIGN AND ANOVA (On Weiss Stats CD).
Module A. Multiple Regression Analysis.
The Multiple Linear Regression Model.
Estimation of the Regression Parameters.
Inferences Concerning the Utility of the Regression Model.
Inferences Concerning the Utility of Particular Predictor Variables.
Confidence Intervals for Mean Response; Prediction.
Intervals for Response.
Checking Model Assumptions and Residual Analysis.
Module B. Model Building in Regression.
Transformations to Remedy Model Violations.
Polynomial Regression Model.
Qualitative Predictor Variables.
Multicollinearity.
Model Selection: Stepwise Regression.
Model Selection: All Subsets Regression.
Pitfalls and Warnings.
Module C. Design Of Experiments and Analysis of Variance.
Factorial Designs.
Two-Way ANOVA: The Logic.
Two-Way ANOVA: The Procedure.
Two-Way ANOVA: Multiple Comparisons.
Randomized Block Designs.
Randomized Block ANOVA: The Logic.
Randomized Block ANOVA: The Procedure.
Randomized Block ANOVA: Multiple Comparisons.
*Friedman's Nonparametric Test for the Randomized Block Design.
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.
III. PROBABILITY, RANDOM VARIABLES, AND SAMPLING DISTRIBUTIONS.
4. Probability Concepts.
Case Study: The Powerball.
Probability Basics.
Events.
Some Rules of Probability.
*Contingency Tables; Joint and Marginal Probabilities.
*Conditional Probability.
*The Multiplication Rule; Independence.
*Bayes's Rule.
*Counting Rules.
5. Discrete Random Variables.
Case Study: Aces Wild on the Sixth at Oak Hill.
*Discrete Random Variables and Probability Distributions.
*The Mean and Standard Deviation of a Discrete Random Variable.
*The Binomial Distribution.
*The Poisson 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.
*Normal Approximation to the Binomial Distribution .
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.
*Type II Error Probabilities; Power.
P-Values.
Hypothesis Tests for One Population Mean When s Is Unknown.
*The Wilcoxon Signed-Rank Test.
*Which Procedure Should Be Used?
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.
*The Mann-Whitney Test.
Inferences for Two Population Means Using Paired Samples.
*The Paired Wilcoxon Signed-Rank Test.
*Which Procedure Should Be Used?
11. Inferences for Population Standard Deviations.
Case Study: Speaker Woofer Driver Manufacturing.
*Inferences for One Population Standard Deviation.
*Inferences for Two Population Standard Deviations Using Independent Samples.
12. 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.
13. Chi-Square Procedures.
Case Study: Road Rage.
The Chi-Square Distribution.
Chi-Square Goodness-Of-Fit Test.
Contingency Tables; Association.
Chi-Square Independence Test.
V. REGRESSION, CORRELATION, AND ANOVA.
14. 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.
15. 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.
*Testing for Normality.
16. Analysis of Variance (Anova).
Case Study: Heavy Drinking Among College Students.
The F-Distribution.
One-Way ANOVA: The Logic.
One-Way ANOVA: The Procedure.
*Multiple Comparisons.
*The Kruskal-Wallis Test.
VI. MULTIPLE REGRESSION AND MODEL BUILDING; EXPERIMENTAL DESIGN AND ANOVA (On Weiss Stats CD).
Module A. Multiple Regression Analysis.
The Multiple Linear Regression Model.
Estimation of the Regression Parameters.
Inferences Concerning the Utility of the Regression Model.
Inferences Concerning the Utility of Particular Predictor Variables.
Confidence Intervals for Mean Response; Prediction.
Intervals for Response.
Checking Model Assumptions and Residual Analysis.
Module B. Model Building in Regression.
Transformations to Remedy Model Violations.
Polynomial Regression Model.
Qualitative Predictor Variables.
Multicollinearity.
Model Selection: Stepwise Regression.
Model Selection: All Subsets Regression.
Pitfalls and Warnings.
Module C. Design Of Experiments and Analysis of Variance.
Factorial Designs.
Two-Way ANOVA: The Logic.
Two-Way ANOVA: The Procedure.
Two-Way ANOVA: Multiple Comparisons.
Randomized Block Designs.
Randomized Block ANOVA: The Logic.
Randomized Block ANOVA: The Procedure.
Randomized Block ANOVA: Multiple Comparisons.
*Friedman's Nonparametric Test for the Randomized Block Design.
APPENDIXES.
Appendix A. Statistical Tables.
Appendix B. Answers To Selected Exercises.
Index.