Skip to content

Intro Stats

ISBN-10: 0321825276

ISBN-13: 9780321825278

Edition: 4th 2014

Authors: Richard D. De Veaux, Paul F. Velleman, David E. Bock, Paul D. Velleman

List price: $224.20
Blue ribbon 30 day, 100% satisfaction guarantee!
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!

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 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.
Customers also bought

Book details

List price: $224.20
Edition: 4th
Copyright year: 2014
Publisher: Prentice Hall PTR
Publication date: 12/29/2012
Binding: Mixed Media
Pages: 800
Size: 8.50" wide x 11.00" long x 1.25" tall
Weight: 4.422
Language: English

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