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INTRODUCTION TO STATISTICS AND DATA ANALYSIS introduces you to the study of statistics and data analysis by using real data and attention-grabbing examples. The authors guide you through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including frequent substitution of words for symbols--helps you grasp concepts and cement your comprehension. You'll also find coverage of most major technologies as a problem-solving tool, plus hands-on activities in each chapter that allow you to practice statistics firsthand.

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• 1 The Role Of Statistics And The Data Analysis Process: Why Study Statistics? The Nature and Role of Variability
• Statistics and the Data Analysis Process
• Types of Data and Some Simple Graphical Displays
• 2 Collecting Data Sensibly: Statistical Studies: Observation and Experimentation
• Sampling
• Simple Comparative Experiments
• More on Experimental Design
• Interpreting and Communicating the Results of Statistical Analyses
• More on Observational Studies: Designing Surveys (online)
• 3 Graphical Methods For Describing Data: Displaying Categorical Data: Comparative Bar Charts and Pie Charts
• Displaying Numerical Data: Stem-and-Leaf Displays
• Displaying Numerical Data: Frequency Distributions and Histograms
• Displaying Bivariate Numerical Data
• Interpreting and Communicating the Results of Statistical Analyses
• 4 Numerical Methods For Describing Data: Describing the Center of a Data Set
• Describing Variability in a Data Set
• Summarizing a Data Set: Boxplots
• Interpreting Center and Variability: Chebyshev?s Rule, the Empirical Rule, and z Scores
• Interpreting and Communicating the Results of Statistical Analyses
• 5 Summarizing Bivariate Data: Correlation
• Linear Regression: Fitting a Line to Bivariate Data
• Assessing the Fit of a Line
• Nonlinear Relationships and Transformations
• Interpreting and Communicating the Results of Statistical Analyses
• Logistic Regression (online)
• 6 Probability: Chance Experiments and Events
• Definition of Probability
• Basic Properties of Probability
• Conditional Probability
• Independence
• Some General Probability Rules
• Estimating Probabilities Empirically Using Simulation
• 7 Random Variables And Probability Distributions: Random Variables
• Probability Distributions for Discrete Random Variables
• Probability Distributions for Continuous Random Variables
• Mean and Standard Deviation of a Random Variable
• Binomial and Geometric Distributions
• Normal Distributions
• Checking for Normality and Normalizing Transformations
• Using the Normal Distribution to Approximate a Discrete Distribution
• 8 Sampling Variability And Sampling Distributions: Statistics and Sampling Variability
• The Sampling Distribution of a Sample Mean
• The Sampling Distribution of a Sample Proportion
• 9 Estimation Using a Single Sample: Point Estimation
• Large-Sample Confidence Interval for a Population Proportion
• Confidence Interval for a Population Mean
• Interpreting and Communicating the Results of Statistical Analyses
• 10 Hypothesis Testing Using A Single Sample: Hypotheses and Test Procedures
• Errors in Hypothesis Testing
• Large-Sample Hypothesis Tests for a Population Proportion
• Hypothesis Tests for a Population Mean
• Power and Probability of Type II Error
• Interpreting and Communicating the Results of Statistical Analyses
• 11 Comparing Two Populations Or Treatments: Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples
• Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples
• Large-Sample Inferences Concerning the Difference Between Two Population or Treatment Proportions
• Interpreting and Communicating the Results of Statistical Analyses
• 12 The Analysis Of Categorical Data And Goodness-Of-Fit Tests: Chi-Square Tests for Univariate Data
• Tests for Homogeneity and Independence in a Two-way Table
• Interpreting and Communicating the Results of Statistical Analyses
• 13 Simple Linear Regression And Correlation: Inferential Methods: Simple Linear Regression Model
• Inferences about the Slope of the Population Regression Line
• Checking Model Adequacy
• Inferences Based on the Estimated Regression Line (online)
• Inferences About the Population Correlation Coefficient (online)
• Interpreting and Communicating the Results of Statistical Analyses (online)
• 14 Multiple Regression Analysis: Multiple Regression Models
• Fitting a Model and Assessing Its Utility
• Inferences Based on an Estimated Model (online)
• Other Issues in Multiple Regres