Statistics : the art and science of learning from data
著者
書誌事項
Statistics : the art and science of learning from data
Pearson Education, c2023
5th ed., Global ed.
- : [pbk.]
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注記
Includes indexes
内容説明・目次
内容説明
Statistics: The Art and Science of Learning From Data, 5th Edition helps you understand what statistics is all about and learn the right questions to ask when analyzing data, instead of just memorizing procedures. It makes accessible the ideas that have turned statistics into a central science of modern life, without compromising essential material. Students often find this book enjoyable to read and stay engaged with the wide variety of real-world data in the examples and exercises. Based on the authors' belief that it's important for you to learn and analyze both quantitative and categorial data, this text pays greater attention to the analysis of proportions than many other introductory statistics texts.
Key features include:
Greater attention to the analysis of proportions compared to other introductory statistics texts.
Introduction to key concepts, presenting the categorical data first, and quantitative data after.
A wide variety of real-world data in the examples and exercises
New sections and updated content will enhance your learning and understanding.
Pearson MyLab (R) Students, if Pearson Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.
目次
PART I: GATHERING AND EXPLORING DATA
Statistics: The Art and Science of Learning from Data
Using Data to Answer Statistical Questions
Sample Versus Population
Organizing Data, Statistical Software, and the New Field of Data Science
Chapter Summary
Chapter Exercises
Exploring Data with Graphs and Numerical Summaries
Different Types of Data
Graphical Summaries of Data
Measuring the Center of Quantitative Data
Measuring the Variability of Quantitative Data
Using Measures of Position to Describe Variability
Linear Transformations and Standardizing
Recognizing and Avoiding Misuses of Graphical Summaries
Chapter Summary
Chapter Exercises
Exploring Relationships Between Two Variables
The Association Between Two Categorical Variables
The Relationship Between Two Quantitative Variables
Linear Regression: Predicting the Outcome of a Variable
Cautions in Analyzing Associations
Chapter Summary
Chapter Exercises
Gathering Data
Experimental and Observational Studies
Good and Poor Ways to Sample
Good and Poor Ways to Experiment
Other Ways to Conduct Experimental and Nonexperimental Studies
Chapter Summary
Chapter Exercises
PART II: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLINGDISTRIBUTIONS
Probability in Our Daily Lives
How Probability Quantifies Randomness
Finding Probabilities
Conditional Probability
Applying the Probability Rules
Chapter Summary
Chapter Exercises
Random Variables and Probability Distributions
Summarizing Possible Outcomes and Their Probabilities
Probabilities for Bell-Shaped Distributions
Probabilities When Each Observation Has Two Possible Outcomes
Chapter Summary
Chapter Exercises
Sampling Distributions
How Sample Proportions Vary Around the Population Proportion
How Sample Means Vary Around the Population Mean
Using the Bootstrap to Find Sampling Distributions
Chapter Summary
Chapter Exercises
PART III: INFERENTIAL STATISTICS
Statistical Inference: Confidence Intervals
Point and Interval Estimates of Population Parameters
Confidence Interval for a Population Proportion
Confidence Interval for a Population Mean
Bootstrap Confidence Intervals
Chapter Summary
Chapter Exercises
Statistical Inference: Significance Tests About Hypotheses
Steps for Performing a Significance Test
Significance Tests About Proportions
Significance Tests About a Mean
Decisions and Types of Errors in Significance Tests
Limitations of Significance Tests
The Likelihood of a Type II Error
Chapter Summary
Chapter Exercises
Comparing Two Groups
Categorical Response: Comparing Two Proportions
Quantitative Response: Comparing Two Means
Comparing Two Groups with Bootstrap or Permutation Resampling
Analyzing Dependent Samples
Adjusting for the Effects of Other Variables
Chapter Summary
Chapter Exercises
PART IV: ANALYZING ASSOCIATION AND EXTENDED STATISTICALMETHODS
Analyzing the Association Between Categorical Variables
Independence and Dependence (Association)
Testing Categorical Variables for Independence
Determining the Strength of the Association
Using Residuals to Reveal the Pattern of Association
Fisher's Exact and Permutation Tests
Chapter Summary
Chapter Exercises
Analyzing the Association Between Quantitative Variables: Regression Analysis
Modeling How Two Variables Are Related
Inference About Model Parameters and the Association
Describing the Strength of Association
How the Data Vary Around the Regression Line
Exponential Regression: A Model for Nonlinearity
Chapter Summary
Chapter Exercises
Multiple Regression
Using Several Variables to Predict a Response
Extending the Correlation and R2 for Multiple Regression
Using Multiple Regression to Make Inferences
Checking a Regression Model Using Residual Plots
Regression and Categorical Predictors
Modeling a Categorical Response
Chapter Summary
Chapter Exercises
Comparing Groups: Analysis of Variance Methods
One-Way ANOVA: Comparing Several Means
Estimating Differences in Groups for a Single Factor
Two-Way ANOVA
Chapter Summary
Chapter Exercises
Nonparametric Statistics
Compare Two Groups by Ranking
Nonparametric Methods for Several Groups and for Matched Pairs
Chapter Summary
Chapter Exercises
Appendix
Answers
Index
Index of Applications
Credits
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