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    Intro Stats

    ISBN-10: 0321825276
    ISBN-13: 9780321825278
    Author(s): Richard D. De Veaux, Paul F. Velleman, David E. Bock
    Description: Richard De Veaux, Paul Velleman, and David Bock wroteIntro Statswith the goal that you have as much fun reading it as they did in writing it. Maintaining a conversational, humorous, and informal writing style, this new edition engages readers from  More...
    List price: $196.00
    Buy it from: $63.19
    Rent it from: $18.60
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    List Price: $196.00
    Edition: 4th
    Publisher: Prentice Hall PTR
    Binding: Mixed Media
    Pages: 800
    Size: 8.50" wide x 10.87" long x 0.59" tall
    Weight: 4.620
    Language: English

    Richard De Veaux, Paul Velleman, and David Bock wroteIntro Statswith the goal that you have as much fun reading it as they did in writing it. Maintaining a conversational, humorous, and informal writing style, this new edition engages readers from the first page. The authors focus on statistical thinking throughout the text and rely on technology for calculations. As a result, students can focus on developing their conceptual understanding. Innovative Think/Show/Tell examples provide a problem-solving framework and, more importantly, a way to think through any statistics problem and present their results. New to theFourth Editionis a streamlined presentation that keeps students focused on what’s most important, while including out helpful features. An updated organization divides chapters into sections, with specific learning objectives to keep students on track. A detailed table of contents assists with navigation through this new layout. Single-concept exercises complement the existing mid- to hard-level exercises for basic skill development.

    Preface
    Index of Applications
    Exploring and Understanding Data
    Stats Starts Here!
    What Is Statistics?
    Data
    Variables
    Displaying and Describing Categorical Data
    Summarizing and Displaying a Single Categorical Variable
    Exploring the Relationship Between Two Categorical Variables
    Displaying and Summarizing Quantitative Data
    Displaying Quantitative Variables
    Shape
    Center
    Spread
    Boxplots and 5-Number Summaries
    The Center of Symmetric Distributions: The Mean
    The Spread of Symmetric Distributions: The Standard Deviation
    Summary-What to Tell About a Quantitative Variable
    Understanding and Comparing Distributions
    Comparing Groups with Histograms
    Comparing Groups with Boxplots
    Outliers
    Timeplots: Order, Please!
    Re-expressing Data: A First Look
    The Standard Deviation as a Ruler and the Normal Model
    Standardizing with z-Scores
    Shifting and Scaling
    Normal Models
    Finding Normal Percentiles
    Normal Probability Plots
    Exploring and Understanding Data
    Exploring Relationships Between Variables
    Scatterplots, Association, and Correlation
    Scatterplots
    Correlation
    Warning: Correlation ≠ Causation
    Straightening Scatterplots
    Linear Regression
    Least Squares: The Line of "Best Fit"
    The Linear Model
    Finding the Least Squares Line
    Regression to the Mean
    Examining the Residuals
    R2-The Variation Accounted for by the Model
    Regression Assumptions and Conditions
    Regression Wisdom
    Examining Residuals
    Extrapolation: Reaching Beyond the Data
    Outliers, Leverage, and Influence
    Lurking Variables and Causation
    Working with Summary Values
    Exploring Relationships Between Variables
    Gathering Data
    Understanding Randomness
    What is Randomness?
    Simulating By Hand
    Sample Surveys
    The Three Big Ideas of Sampling
    Populations and Parameters
    Simple Random Samples
    Other Sampling Designs
    From the Population to the Sample: You Can't Always Get What You Want
    The Valid Survey
    Common Sampling Mistakes, or How to Sample Badly
    Experiments and Observational Studies
    Observational Studies
    Randomized, Comparative Experiments
    The Four Principles of Experimental Design
    Control Treatments
    Blocking
    Confounding
    Gathering Data
    Randomness and Probability
    From Randomness to Probability
    Random Phenomena
    Modeling Probability
    Formal Probability
    Probability Rules!
    The General Addition Rule
    Conditional Probability and the General Multiplication Rule
    Independence
    Picturing Probability: Tables, Venn Diagrams and Trees
    Reversing the Conditioning and Bayes' Rule
    Random Variables and Probability Models
    Expected Value: Center
    Standard Deviation
    Combining Random Variables
    The Binomial Model
    Modeling the Binomial with a Normal Model
    The Poisson Model
    Continuous Random Variables
    Randomness and Probability
    From the Data at Hand to the World at Large
    Sampling Distribution Models
    Sampling Distribution of a Proportion
    When Does the Normal Model Work? Assumptions and Conditions
    The Sampling Distribution of Other Statistics
    The Central Limit Theorem: The Fundamental Theorem of Statistics
    Sampling Distributions: A Summary
    Confidence Intervals for Proportions
    A Confidence Interval
    Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
    Margin of Error: Certainty vs. Precision
    Assumptions and Conditions
    Testing Hypotheses About Proportions
    Hypotheses
    P-Values
    The Reasoning of Hypothesis Testing
    Alternative Alternatives
    P-Values and Decisions: What to Tell About a Hypothesis Test
    Inferences About Means
    Getting Started: The Central Limit Theorem (Again)
    Gosset's t
    Interpreting Confidence Intervals
    A Hypothesis Test for the Mean
    Choosing the Sample Size
    More About Tests and Intervals
    Choosing Hypotheses
    How to Think About P Values
    Alpha Levels
    Practical vs. Statistical Significance
    Critical Values Again
    Errors
    Power
    From the Data at Hand to the World at Large
    Learning About the World
    Comparing Groups
    The Variance of a Difference
    The Standard Deviation of the Difference Between Two Proportions
    Assumptions and Conditions for Comparing Proportions
    The Sampling Distribution of the Difference between Two Proportions
    Comparing Two Means
    The Two-Sample t-Test: Testing for the Difference Between Two Means
    The Two Sample z-Test: Testing for the Difference between Proportions
    The Pooled t-Test: Everyone into the Pool?
    Pooling
    Paired Samples and Blocks
    Paired Data
    Assumptions and Conditions
    Confidence Intervals for Matched Pairs
    Blocking
    Comparing Counts
    Goodness-of-Fit Tests
    Chi-Square Test of Homogeneity
    Examining the Residuals
    Chi-Square Test of Independence
    Inferences for Regression
    The Population and the Sample
    Assumptions and Conditions
    Intuition About Regression Inference
    Regression Inference
    Standard Errors for Predicted Values
    Confidence Intervals for Predicted Values
    Logistic Regression
    Learning About the World
    Inference When Variables Are Related
    Analysis of Variance
    Testing Whether the Means of Several Groups Are Equal
    The ANOVA Table
    Plot the Data…
    Comparing Means
    Multiple Regression
    Two Predictors
    What Multiple Regression Coefficients Mean
    The Multiple Regression Model
    Multiple Regression Inference
    Comparing Multiple Regression Models
    Appendices
    Answers
    Photo Acknowledgments
    Index
    Tables and Selected Formulas

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