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Author: James T McClave; Terry Sincich
Publisher: Upper Saddle River, N.J. : Pearson Prentice Hall, ©2009.
Edition/Format:   Print book : CD for computer : English : 11th edView all editions and formats

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Additional Physical Format: Online version:
McClave, James T.
Upper Saddle River, N.J. : Pearson Prentice Hall, ©2009
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: James T McClave; Terry Sincich
ISBN: 9780132069519 0132069512 9780132363440 0132363445 9780136001829 0136001823
OCLC Number: 165958285
Description: xxii, 835 pages : color illustrations ; 29 cm + 1 CD-ROM (4 3/4 in.)
Contents: 1. Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications 1.3 Fundamental Elements of Statistics 1.4 Types of Data 1.5 Collecting Data 1.6 The Role of Statistics in Critical Thinking Statistics in Action: USA Weekend Teen Surveys -- Are Boys Really from Mars and Girls from Venus? Using Technology: Creating and Listing Data in MINITAB 2. Methods for Describing Sets of Data 2.1 Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Summation Notation 2.4 Numerical Measures of Central Tendency 2.5 Numerical Measures of Variability 2.6 Interpreting the Standard Deviation 2.7 Numerical Measures of Relative Standing 2.8 Methods for Detecting Outliers (Optional) 2.9 Graphing Bivariate Relationships (Optional) 2.10 Distorting the Truth with Descriptive Techniques Statistics In Action: The "Eye Cue" Test: Does Experience Improve Performance? Using Technology: Describing Data in MINITAB 3. Probability 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 3.7 Random Sampling 3.8 Some Counting Rules (Optional) 3.9 Bayes' Rule (Optional) Statistics In Action: Lotto Buster! -- Can You Improve Your Chances of Winning the Lottery? Using Technology: Generating a Random Sample in MINITAB 4. Discrete Random Variables 4.1 Two Types of Random Variables 4.2 Probability Distributions for Discrete Random Variables 4.3 Expected Values of Discrete Random Variables 4.4 The Binomial Random Variable 4.5 The Poisson Random Variable (Optional) 4.6 The Hypergeometric Random Variable (Optional) Statistics in Action: Probability in a Reverse Cocaine Sting -- Was Cocaine Really Sold? Using Technology: Binomial, Poisson, and Hypergeometric Probabilities in MINITAB 5. Continuous Random Variables 5.1 Continuous Probability Distributions 5.2 The Uniform Distribution 5.3 The Normal Distribution 5.4 Descriptive Methods for Assessing Normality 5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional) 5.6 The Exponential Distribution (Optional) Statistics in Action: Super Weapons Development -- Is the Hit Ratio Optimized? Using Technology: Normal Probability Plots in MINITAB 6. Sampling Distributions 6.1 What is a Sampling Distribution? 6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional) 6.3 The Central Limit Theorem Statistics in Action: The Insomnia Pill -- Will It Take Less Time to Fall Asleep? Using Technology: Simulating a Sampling Distribution in MINITAB 7. Inferences Based on a Single Sample: Estimation with Confidence Intervals 7.1 Identifying the Target Parameter 7.2 Large-Sample Confidence Interval for a Population Mean 7.3 Small-Sample Confidence Interval for a Population Mean 7.4 Large-Sample Confidence Interval for a Population Proportion 7.5 Determining the Sample Size Statistics in Action: Speed--Can a High School Football Player Improve His Sprint Time? Using Technology: Confidence Intervals in MINITAB 8. Inferences Based on a Single Sample: Tests of Hypothesis 8.1 The Elements of a Test of Hypothesis 8.2 Large-Sample Test of Hypothesis About a Population Mean 8.3 Observed Significance Levels: p-Values 8.4 Small-Sample Test of Hypothesis About a Population Mean 8.5 Large-Sample Test of Hypothesis About a Population Proportion 8.6 Calculating Type II Error Probabilities: More About beta (Optional) 8.7 Test of Hypothesis About a Population Variance (Optional) Statistics in Action: Diary of a Kleenex User -- How Many Tissues in a Box? Using Technology: Tests of Hypothesis in MINITAB 9. Inferences Based on a Two Samples: Confidence Intervals and Tests of Hypotheses 9.1 Identifying the Target Parameter 9.2 Comparing Two Population Means: Independent Sampling 9.3 Comparing Two Population Means: Paired Difference Experiments 9.4 Comparing Two Population Proportions: Independent Sampling 9.5 Determining the Sample Size 9.6 Comparing Two Population Variances: Independent Sampling (Optional) Statistics in Action: Do Homework Assignments Designed to Involve Family Members Really Work? Using Technology: Two-Sample Inferences in MINITAB 10. Analysis of Variance: Comparing More Than Two Means 10.1 Elements of a Designed Experiment 10.2 The Completely Randomized Design 10.3 Multiple Comparisons of Means 10.4 The Randomized Block Design 10.5 Factorial Experiments Statistics in Action: On the Trail of the Cockroach: Do Roaches Travel at Random? Using Technology: Analysis of Variance in MINITAB 11. Simple Linear Regression 11.1 Probabilistic Models 11.2 Fitting the Model: The Least Squares Approach 11.3 Model Assumptions 11.4 Assessing the Utility of the Model: Making Inferences About the Slope beta1 11.5 The Coefficients of Correlation and Determination 11.6 Using the Model for Estimation and Prediction 11.7 A Complete Example Statistics in Action: Can "Dowsers" Really Detect Water? Using Technology: Simple Linear Regression in MINITAB 12. Multiple Regression and Model Building 12.1 Multiple Regression Models 12.2 The First-Order Model: Inferences About the Individual beta-Parameters 12.3 Evaluating the Overall Utility of a Model 12.4 Using the Model for Estimation and Prediction 12.5 Model Building: Interaction Models 12.6 Model Building: Quadratic and other Higher-Order Models 12.7 Model Building: Qualitative (Dummy) Variable Models 12.8 Model Building: Models with both Quantitative and Qualitative Variables 12.9 Model Building: Comparing Nested Models (Optional) 12.10 Model Building: Stepwise Regression (Optional) 12.11 Residual Analysis: Checking the Regression Assumptions 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation Statistics in Action: Modeling Condo Sales: Are There Differences in Auction Prices? Using Technology: Multiple Regression in MINITAB 13. Categorical Data Analysis 13.1 Categorical Data and the Multinomial Distribution 13.2 Testing Categorical Probabilities: One-Way Table 13.3 Testing Categorical Probabilities: Two-Way (Contingency) Table 13.4 A Word of Caution About Chi-Square Tests Statistics in Action: College Students and Alcohol -- Is Drinking Frequency Related to Amount? Using Technology: Chi-Square Analyses in MINITAB 14. Nonparametric Statistics 14.1 Introduction: Distribution-Free Tests 14.2 Single Population Inferences 14.3 Comparing Two Populations: Independent Samples 14.4 Comparing Two Populations: Paired Difference Experiment 14.5 Comparing Three or More Populations: Completely Randomized Design 14.6 Comparing Three or More Populations: Randomized Block Design 14.7 Rank Correlation Statistics in Action: How Vulnerable are Wells to Groundwater Contamination? Using Technology: Nonparametric Analyses in MINITAB Appendix A: Tables Table I Random Numbers Table II Binomial Probabilities Table III Poisson Probabilities Table IV Normal Curve Areas Table V Exponentials Table VI Critical Values of t Table VII Critical Values of chi2 Table VIII Percentage Points of the F Distribution, alpha=.10 Table IX Percentage Points of the F Distribution, alpha=.05 Table X Percentage Points of the F Distribution, alpha=.025 Table XI Percentage Points of the F Distribution, alpha=.01 Table XII Critical Values of TL and TU for the Wilcoxon Rank Sum Test Table XIII Critical Values of T0 in the Wilcoxon Signed Rank Test Table XIV Critical Values of Spearman's Rank Correlation Coefficient Appendix B: Calculation Formulas for Analysis of Variance Short Answers to Selected Odd-Numbered Exercises
Responsibility: James T. McClave, Terry Sincich.
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