ISBN-10: 0321891910

ISBN-13: 9780321891914

Edition: 12th 2013

List price: $241.80
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Classic, yet contemporary. Theoretical, yet applied. McClave & Sincich’sStatisticsgives you the best of both worlds. This text offers a trusted, comprehensive introduction to statistics that emphasizes inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data. TheTwelfth Editioninfuses a new focus on ethics, which is critically important when working with statistical data. Chapter Summaries have a new, study-oriented design, helping students stay focused when preparing for exams. Data, exercises, technology support, and Statistics in Action cases are updated throughout the book. 0321837746 / 9780321837745 Statistics plus MyStatLab Student Access KitPackage consists of:   0321694635 / 9780321694638 MyStatLab -- Access Card0321755936 / 9780321755933 Statistics 
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Book details

List price: $241.80
Edition: 12th
Copyright year: 2013
Publisher: Addison Wesley
Publication date: 8/9/2012
Binding: Mixed Media
Size: 9.00" wide x 11.25" long x 1.50" tall
Weight: 4.048
Language: English

James T. McClave, Info Tech, Inc./ University of Florida P. Goerge Benson, Terry College of Business, University of Georgia Terry Sincich, University of South Florida

Statistics, Data, and Statistical Thinking
the Science of Statistics
Types of Statistical Applications
Fundamental Elements of Statistics
Types of Data
Collecting Data
the Role of Statistics in Critical Thinking
Statistics in Action: Social Media Networks and the Millennial Generation
Using Technology: Creating and Listing Data
Methods for Describing Sets of Data
Describing Qualitative Data
Graphical Methods for Describing Quantitative Data
Summation Notation
Numerical Measures of Central Tendency
Numerical Measures of Variability
Interpreting the Standard Deviation
Numerical Measures of Relative Standing
Methods for Detecting Outliers: Box Plots and z-Scores
Graphing Bivariate Relationships (Optional)
Distorting the Truth with Descriptive Techniques
Statistics in Action: Body Image Dissatisfaction: Real or Imagined?
Using Technology: Describing Data
Events, Sample Spaces, and Probability
Unions and Intersections
Complementary Events
the Additive Rule and Mutually Exclusive Events
Conditional Probability
the Multiplicative Rule and Independent Events
Random Sampling
Some Additional Counting Rules (Optional)
Bayes' Rule (Optional)
Statistics in Action: Lotto Buster! -Can You Improve your Chances of Winning the Lottery?
Using Technology: Generating a Random Sample
Discrete Random Variables
Two Types of Random Variables
Probability Distributions for Discrete Random Variables
Expected Values of Discrete Random Variables
the Binomial Random Variable
the Poisson Random Variable (Optional)
the Hypergeometric Random Variable (Optional)
Statistics in Action: Probability in a Reverse Cocaine Sting- Was Cocaine Really Sold?
Using Technology: Discrete Probabilities
Continuous Random Variables
Continuous Probability Distributions
the Uniform Distribution
the Normal Distribution
Descriptive Methods for Assessing Normality
Approximating a Binomial Distribution with a Normal Distribution (Optional)
the Exponential Distribution (Optional)
Statistics in Action: Super Weapons Development - is the Hit Ratio Optimized?
Using Technology: Continuous Random Variables, Probabilities, and Normal Probability Plots
Sampling Distributions
What is a Sampling Distribution?
Properties of Sampling Distributions: Unbiasedness and Minimum Variance
the Sampling Distribution of (x-bar) and the Central Limit Theorem
Statistics in Action: the Insomnia Pill-Will It Take Less Time to Fall Asleep?
Using Technology: Simulating a Sampling Distribution
Inferences Based on a Single Sample: Estimation with Confidence Intervals
Identifying and Estimating the Target Parameter
Confidence Interval for a Population Mean: Normal (z) Statistic
Confidence Interval for a Population Mean: Student's t-statistic
Large-Sample Confidence Interval for a Population Proportion
Determining the Sample Size
Confidence Interval for a Population Variance (Optional)
Statistics in Action: Medicare Fraud Investigations
Using Technology: Confidence Intervals
Inferences Based on a Single Sample: Tests of Hypothesis
the Elements of a Test of Hypothesis
Formulating Hypotheses and Setting Up the Rejection Region
Test of Hypothesis About a Population Mean: Normal (z) Statistic
Observed Significance Levels: p-Values
Test of Hypothesis About a Population Mean: Student's t-statistic
Large-Sample Test of Hypothesis About a Population Proportion
Calculating Type II Error Probabilities: More About � (Optional)
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
Inferences Based on a Two Samples: Confidence Intervals and Tests of Hypotheses
Identifying the Target Parameter
Comparing Two Population Means: Independent Sampling
Comparing Two Population Means: Paired Difference Experiments
Comparing Two Population Proportions: Independent Sampling
Determining the Sample Size
Comparing Two Population Variances: Independent Sampling (Optional)
Statistics in Action: Zixit Corp. vs. Visa USA Inc.-A Libel Case
Using Technology: Two-Sample Inferences
Analysis of Variance: Comparing More Than Two Means
Elements of a Designed Study
the Completely Randomized Design: Single Factor
Multiple Comparisons of Means
the Randomized Block Design
Factorial Experiments: Two Factors
Statistics in Action: on the Trail of the Cockroach: Do Roaches Travel at Random?
Using Technology: Analysis of Variance
Simple Linear Regression
Probabilistic Models
Fitting the Model: the Least Squares Approach
Model Assumptions
Assessing the Utility of the Model: Making Inferences About the Slope �1
the Coefficients of Correlation and Determination
Using the Model for Estimation and Prediction
A Complete Example
Statistics in Action: Can "Dowsers" Really Detect Water?
Using Technology: Simple Linear Regression
Multiple Regression and Model Building
Multiple Regression Models
the First-Order Model: Inferences About the Individual �-Parameters
Evaluating the Overall Utility of a Model
Using the Model for Estimation and Prediction
Model Building: Interaction Models
Model Building: Quadratic and other Higher-Order Models
Model Building: Qualitative (Dummy) Variable Models
Model Building: Models with both Quantitative and Qualitative Variables
Model Building: Comparing Nested Models (Optional)
Model Building: Stepwise Regression (Optional)
Residual Analysis: Checking the Regression Assumptions
Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
Statistics in Action: Modeling Condo Sales: are There Differences in Auction Prices?
Using Technology: Multiple Regression
Categorical Data Analysis
Categorical Data and the Multinomial Distribution
Testing Categorical Probabilities: One-Way Table
Testing Categorical Probabilities: Two-Way (Contingency) Table
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
Nonparametric Statistics
Introduction: Distribution-Free Tests
Single Population Inferences
Comparing Two Populations: Independent Samples
Comparing Two Populations: Paired Difference Experiment
Comparing Three or More Populations: Completely Randomized Design
Comparing Three or More Populations: Randomized Block Design
Rank Correlation
Statistics in Action: How Vulnerable are Wells to Groundwater Contamination?
Using Technology: Nonparametric Analyses
Random Numbers
Binomial Probabilities
Poisson Probabilities
Normal Curve Areas
Critical Values of t
Critical Values of x2
Percentage Points of the F Distribution, �=.10
Percentage Points of the F Distribution, �=.05
Percentage Points of the F Distribution, �=.025
Percentage Points of the F Distribution, �=.01
Critical Values of TL and TU for the Wilcoxon Rank Sum Test
Critical Values of T0 in the Wilcoxon Signed Rank Test
Critical Values of Spearman's Rank Correlation Coefficient
Calculation Formulas for Analysis of Variance
Short Answers to Selected Odd-Numbered Exercises
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