Statistics: International Edition

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  • Format: Nonspecific Binding
  • Copyright: 2008-01-01
  • Publisher: Pearson Higher Education (UK)
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The Eleventh Edition of this highly-regarded introductory text emphasizes inference and sound decision-making through its extensive coverage of data collection and analysis. McClave develops statistical thinking and teaches students to properly assess the credibility of inferences-from the vantage point of both the consumer and the producer. This edition incorporates more exercises and more visual features, such as redesigned end-of-chapter summaries and an increased use of applets. This text assumes a mathematical background of basic algebra. Datasets and other resources (where applicable) for this book are available here .

Table of Contents

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: USA Weekend
Teen Surveys - Are Boys Really from Mars and Girls from Venus?
Using Technology: Creating and Listing Data in MINITAB
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 (Optional)
Graphing Bivariate Relationships (Optional)
Distorting the Truth with Descriptive Techniques
Statistics In Action: The "Eye Cue" Test: Does Experience Improve Performance?
Using Technology: Describing Data in MINITAB
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 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 in MINITAB
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: Binomial, Poisson, and Hypergeometric Probabilities in MINITAB
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: Normal Probability Plots in MINITAB
Sampling Distributions
What is a Sampling Distribution?
Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional)
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
Inferences Based on a Single Sample: Estimation with Confidence Intervals
Identifying the Target Parameter
Large-Sample Confidence Interval for a Population Mean
Small-Sample Confidence Interval for a Population Mean
Large-Sample Confidence Interval for a Population Proportion
Determining the Sample Size
Statistics in Action: Speed-Can a High School Football Player Improve His Sprint Time?
Using Technology: Confidence Intervals in MINITAB
Inferences Based on a Single Sample: Tests of Hypothesis
The Elements of a Test of Hypothesis
Large-Sample Test of Hypothesis About a Population Mean
Observed Significance Levels: p-Values
Small-Sample Test of Hypothesis About a Population Mean
Large-Sample Test of Hypothesis About a Population Proportion
Calculating Type II Error Probabilities: More About ß (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 in MINITAB
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: Do Homework Assignments Designed to Involve Family Members Really Work?
Using Technology: Two-Sample Inferences in MINITAB
Analysis of Variance: Comparing More Than Two Means
Elements of a Designed Experiment
The Completely Randomized Design
Multiple Comparisons of Means
The Randomized Block Design
Factorial Experiments
Statistics in Action: On the Trail of the Cockroach: Do Roaches Travel at Random?
Using Technology: Analysis of Variance in MINITAB
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 ß1
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 in MINITAB
Multiple Regression and Model Building
Multiple Regression Models
The First-Order Model: Inferences About the Individual ß-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 in MINITAB
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 in MINITAB
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 in MINITAB
Binomial Probabilities
Poisson Probabilities
Normal Curve Areas
Critical Values of t
Critical Values of ¿2
Percentage Points of the F Distribution, ¿=p. 10
Percentage Points of the F Distribution, ¿=p. 05
Percentage Points of the F Distribution, ¿=p. 025
Percentage Points of the F Distribution, ¿=p. 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
Table of Contents provided by Publisher. All Rights Reserved.

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