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STAT2 : building models for a world of data /

STAT2 is designed to help students build on their statistical knowledge in order to analyse rich datasets using statistical models. The book develops a systematic approach to using different models and includes exercises to allow students to practice working with real data alongside output from stat...

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Bibliographic Details
Main Author: Cannon, Ann R. (Author)
Format: Book
Language:English
Published: New York : W.H. Freeman, [2013].
Subjects:
Mathematical statistics > Study and teaching (Higher)
Statistics > Study and teaching (Higher)
Database management > Statistics.
Table of Contents:
  • Machine generated contents note: 0.What Is a Statistical Model?
  • 0.1.Fundamental Terminology
  • 0.2.Four-Step Process
  • 0.3.Chapter Summary
  • 0.4.Exercises
  • Unit A Linear Regression
  • 1.Simple Linear Regression
  • 1.1.The Simple Linear Regression Model
  • 1.2.Conditions for a Simple Linear Model
  • 1.3.Assessing Conditions
  • 1.4.Transformations
  • 1.5.Outliers and Influential Points
  • 1.6.Chapter Summary
  • 1.7.Exercises
  • 2.Inference for Simple Linear Regression
  • 2.1.Inference for Regression Slope
  • 2.2.Partitioning Variability
  • ANOVA
  • 2.3.Regression and Correlation
  • 2.4.Intervals for Predictions
  • 2.5.Chapter Summary
  • 2.6.Exercises
  • 3.Multiple Regression
  • 3.1.Multiple Linear Regression Model
  • 3.2.Assessing a Multiple Regression Model
  • 3.3.Comparing Two Regression Lines
  • 3.4.New Predictors from Old
  • 3.5.Correlated Predictors
  • 3.6.Testing Subsets of Predictors
  • 3.7.Case Study: Predicting in Retail Clothing
  • 3.8.Chapter Summary
  • Note continued: 3.9.Exercises
  • 4.Additional Topics in Regression
  • 4.1.Topic: Added Variable Plots
  • 4.2.Topic: Techniques for Choosing Predictors
  • 4.3.Topic: Identifying Unusual Points in Regression
  • 4.4.Topic: Coding Categorical Predictors
  • 4.5.Topic: Randomization Test for a Relationship
  • 4.6.Topic: Bootstrap for Regression
  • 4.7.Exercises
  • Unit B Analysis of Variance
  • 5.One-Way ANOVA
  • 5.1.The One-Way Model: Comparing Groups
  • 5.2.Assessing and Using the Model
  • 5.3.Scope of Inference
  • 5.4.Fisher's Least Significant Difference
  • 5.5.Chapter Summary
  • 5.6.Exercises
  • 6.Multifactor ANOVA
  • 6.1.The Two-Way Additive Model (Main Effects Model)
  • 6.2.Interaction in the Two-Way Model
  • 6.3.Two-Way Nonadditive Model (Two-Way ANOVA with Interaction)
  • 6.4.Case Study
  • 6.5.Chapter Summary
  • 6.6.Exercises
  • 7.Additional Topics in Analysis of Variance
  • 7.1.Topic: Levene's Test for Homogeneity of Variances
  • 7.2.Topic: Multiple Tests
  • Note continued: 7.3.Topic: Comparisons and Contrasts
  • 7.4.Topic: Nonparametric Statistics
  • 7.5.Topic: ANOVA and Regression with Indicators
  • 7.6.Topic: Analysis of Covariance
  • 7.7.Exercises
  • 8.Overview of Experimental Design
  • 8.1.Comparisons and Randomization
  • 8.2.Randomization F-Test
  • 8.3.Design Strategy: Blocking
  • 8.4.Design Strategy: Factorial Crossing
  • 8.5.Chapter Summary
  • 8.6.Exercises
  • Unit C Logistic Regression
  • 9.Logistic Regression
  • 9.1.Choosing a Logistic Regression Model
  • 9.2.Logistic Regression and Odds Ratios
  • 9.3.Assessing the Logistic Regression Model
  • 9.4.Formal Inference: Tests and Intervals
  • 9.5.Chapter Summary
  • 9.6.Exercises
  • 10.Multiple Logistic Regression
  • 10.1.Overview
  • 10.2.Choosing, Fitting, and Interpreting Models
  • 10.3.Checking Conditions
  • 10.4.Formal Inference: Tests and Intervals
  • 10.5.Case Study: Bird Nests
  • 10.6.Chapter Summary
  • 10.7.Exercises
  • Note continued: 11.Additional Topics in Logistic Regression
  • 11.1.Topic: Fitting the Logistic Regression Model
  • 11.2.Topic: Assessing Logistic Regression Models
  • 11.3.Randomization Tests for Logistic Regression
  • 11.4.Analyzing Two-Way Tables with Logistic Regression
  • 11.5.Exercises
  • Short Answers
  • Indexes
  • General Index.