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Produktbild: Osborne, J: Regression & Linear Modeling

Osborne, J: Regression & Linear Modeling Best Practices and Modern Methods

Fr. 239.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.05.2016

Verlag

O'Reilly

Seitenzahl

488

Maße (L/B/H)

25.8/18/3 cm

Gewicht

1100 g

Sprache

Englisch

ISBN

978-1-5063-0276-8

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.05.2016

Verlag

O'Reilly

Seitenzahl

488

Maße (L/B/H)

25.8/18/3 cm

Gewicht

1100 g

Sprache

Englisch

ISBN

978-1-5063-0276-8

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  • Produktbild: Osborne, J: Regression & Linear Modeling
  • Chapter 1: A Nerdly Manifesto
    The Variables Lead the Way
    Different Classifications of Measurement
    It's All About Relationships!
    A Brief Review of Basic Algebra and Linear Equations
    The GLM in One Paragragh
    A Brief Consideration of Prediction
    A Brief Primer on Null Hypothesis Statistical Testing
    A Tale of Two Errors
    What Conclusions Can We Draw Based on NHST Results?
    So What Does Failure to Reject the Null Hypothesis Mean?
    Moving Beyond NHST
    The Importance of Replication and Generalizability
    Where We Go From Here
    Enrichment
    Chapter 2: Basic Estimation and Assumptions
    Estimation and the GLM
    What Is OLS Estimation?
    ML Estimation-A Gentle but Deeper Look
    Assumptions for OLS and ML Estimation
    Simple Univariate Data Cleaning and Data Transformations
    What If We Cannot Meet the Assumptions?
    Where We Go From Here
    Enrichment
    Chapter 3: Simple Linear Models With Continuous Dependent Variables: Simple Regression Analyses
    Advance Organizer
    It's All About Relationships!
    Basics of the Pearson Product-Moment Correlation Coefficient
    Calculating r
    Effect Sizes and r
    A Real Data Example
    The Basics of Simple Regression
    Basic Calculations for Simple Regression
    Standardized Versus Unstandardized Regression Coefficients
    Hypothesis Testing in Simple Regression
    A Real Data Example
    Does Centering or z-Scoring Make a Difference?
    Some Simple Multivariate Data Cleaning
    Summary
    Enrichment
    Chapter 4: Simple Linear Models With Continuous Dependent Variables: Simple ANOVA Analyses
    Advance Organizer
    It's All About Relationships! (Part 2)
    Analyzing These Data via t-Test
    Analyzing These Data via ANOVA
    ANOVA Within an OLS Regression Framework
    When Your IV Has More Than Two Groups: Dummy Coding Your Unordered Polytomous Variable
    Smoking and Diabetes Analyzed via ANOVA
    Smoking and Diabetes Analyzed via Regression
    What If the Dummy Variables Are Coded Differently?
    Unweighted Effects Coding
    Weighted Effects Coding
    Common Alternatives to Dummy or Effects Coding
    Summary
    Enrichment
    Chapter 5: Simple Linear Models With Categorical Dependent Variables: Binary Logistic Regression
    Advance Organizer
    It's All About Relationships! (Part 3)
    The Linear Probability Model
    How Logistic Regression Solves This Issue: The Logit Link Function
    A Brief Digression Into Probabilities, Conditional Probabilities, and Odds
    Simple Logistic Regression Using Statistical Software
    The Logistic Regression Equation
    Interpreting the Constant
    What If You Want CIs for the Constant?
    Summary So Far
    Logistic Regression With a Continuous IV
    Some Best Practices When Using a Continuous Variable in Logistic Regression
    Testing Assumptions and Data Cleaning in Logistic Regression
    Hosmer and Lemeshow Test for Model Fit
    Summary
    Enrichment
    Appendix 5A: A Brief Primer in Probit Regression
    Chapter 6: Simple Linear Models With Polytomous Categorical Dependent Variables: Multinomial and Ordinal Logistic Regression
    Advance Organizer
    Understanding Marijuana Use
    Dummy-Coded DVs and Our Hypotheses to Be Tested
    Basics and Calculations
    Multinomial Logistic Regression (Unordered) With Statistical Software
    Multinomial Logistic Regression With a Continuous Predictor
    Multinomial Logistic Regression as a Series of Binary Logistic Regressions
    Data Cleaning and Multinomial Logistic Regression
    Testing Whether Groups Can Be Combined
    Ordered Logit (Proportional Odds) Model
    Assumptions of the Ordinal Logistic Model
    Interpreting the Results of the Ordinal Regression
    Interpreting the Intercepts/Thresholds
    Interpreting the Parameter Estimates
    Data Cleaning and More Advanced Models in Ordinal Logistic Regression
    The Measured Variable is Continous, Why Not Just Use OLS Regression for This Type of Analysis?
    A Brief Note on Log-Linear Analyses
    Summary and Conclusions
    Enrichment
    Chapter 7: Simple Curvilinear Models
    Advance Organizer
    Zeno's Paradox, a Nerdy Science Joke, and Inherent Curvilinearity in the Universe...
    A Brief Review of Simple Algebra
    Hypotheses to Be Tested
    Illegitimate Causes of Curvilinearity
    Detection of Nonlinear Effects
    Basic Principles of Curvilinear Regression
    Curvilinear OLS Regression Example: Size of the University and Faculty Salary
    Data Cleaning
    Interpreting Curvilinear Effects Effectively
    Reality Testing This Effect
    Summary of Curvilinear Effects in OLS Regression
    Curvilinear Logistic Regression Example: Diabetes and Age
    Curvilinear Effects in Multinomial Logistic Regression
    Replication Becomes Important
    More Fun With Curves: Estimating Minima and Maxima as Well as Slope at Any Point on the Curve
    Summary
    Enrichment
    Chapter 8: Multiple Independent Variables
    Advance Organizer
    The Basics of Multiple Predictors
    What Are the Implications of This Act?
    Hypotheses to Be Tested in Multiple Regression
    Assumptions of Multiple Regression and Data Cleaning
    Predicting Student Achievement From Real Data
    Testing Assumptions and Data Cleaning in the NELS88 Data
    Methods of Entering Variables
    Using Multiple Regression for Theory Testing
    Logistic Regression With Multiple IVs
    Assessing the Overall Logistic Regression Model: Why There Is No R2 for Logistic Regression
    Summary and conclusions
    Exercises
    Chapter 9: Interactions Between Independent Variables: Simple moderation
    Advance Organizer
    What is an Interaction?
    Procedural and Conceptual Issues in Testing for Interactions Between Continuous Variables
    Procedural and Conceptual Issues in Testing for Interactions Containing Categorical Variables
    Hypotheses to Be Tested in Multiple Regression With Interactions Present
    An OLS Regression Example: Predicting Student Achievement From Real Data
    Interpreting the Results From a Significant Interaction
    Graphing Interaction Effects
    An Interaction Between a Continuous and a Categorical Variable in OLS Regression
    Interactions With Logistic Regression
    Example Summary of Interaction Analysis
    Interactions and Multinomial Logistic Regression
    Example Summary of Findings
    Can These Effects Replicate?
    Post Hoc Probing of Interactions
    Summary
    Enrichment
    Chapter 10: Curvilinear Interactions Between Independent Variables
    Advance Organizer
    What is a Curvilinear Interaction?
    A Quadratic Interaction Between X and Z
    A Cubic Interaction Between X and Z
    A Real-Data Example and Exploration of Procedural Details
    Curvilinear Interactions Between Continuous and Categorical Variables
    Curvilinear Interactions With Categorical DVs (Multinomial Logistic)
    Curvilinear Interaction Effects in Ordinal Regression
    Chapter Summary
    Enrichment
    Chapter 11: Poisson Models: Low-Frequency Count Data as Dependent Variables
    Advance Organizer
    The Basics and Assumptions of Poisson Regression
    Why Can't We Just Analyze Count Data via OLS, Multinomial, or Ordinal Regression?
    Hypotheses Tested in Poisson Regression
    Poisson Regression With Real Data
    Interactions in Poisson regression
    Data Cleaning in Poisson Regression
    Refining the Model by Eliminating Excess (Inappropriate) Zeros
    A Refined Analysis With Excess Zeros Removed
    Curvilinear Effects in Poisson Regression
    Dealing With Overdispersion or Underdispersion
    Negative Binomial Model
    Summary and Conclusions
    Enrichment
    Chapter 12: Log-Linear Models: General Linear Models When All of Your Variables Are Unordered Categorical
    Advance Organizer
    The Basics of Loglinear Analysis
    Hypotheses Being Tested
    Assumptions of Loglinear Models
    A Slightly More Complex Loglinear Model
    Can We Replicate These Results in Logistic Regression?
    Data Cleaning in Loglinear Models
    Summary and Conclusions
    Enrichment
    Chapter 13: A Brief Introduction to Hierarchical Linear Modeling
    Advance Organizer
    Why HLM models Are Necessary
    How Do Hierarchical Models Work? A Brief Primer
    Generalizing the Basic HLM Model
    Residuals in HLM
    Results of DROPOUT Analysis in HLM
    Summary and Conclusions
    Enrichment
    Chapter 14: Missing Data in Linear Modeling
    Advance Organizer
    Not All Missing Data Are the Same
    Categories of Missingness: Why Do We Care If Data Are MCAR or Not?
    How Do You Know If Your Data Are MCAR, MAR, or MNAR?
    What Do We Do With Randomly Missing Data?
    Data MCAR
    Data MNAR
    How Missingness Can Be an Interesting Variable in and of Itself
    Summing Up: Benefits of Appropriately Handling Missing Data
    Enrichment
    Chapter 15: Trustworthy Science: Improving Statistical Reporting
    Advance Organizer
    What Is Power, and Why Is It Important?
    Power in Linear Models
    Summary of Points Thus Far
    Who Cares as Long as p < .05? Volatility in Linear Models
    A Brief Introduction to Bootstrap Resampling
    Summary and Conclusions
    Enrichment
    Chapter 16: Reliable Measurement Matters
    Advance Organizer
    A More Modern View of Reliability
    What is Cronbach's Alpha (and What Is It Not)?
    Factors That Influence Alpha
    What Is "Good Enough" for Alpha?
    Reliability and Simple Correlation or Regression
    Reliability and Multiple IVs
    Reliability and Interactions in Multiple Regression
    Protecting Against Overcorrecting During Disattenuation
    Other (Better) Solutions to the Issue of Measurement Error
    Does Reliability Influence Other Analyses, Such as Analysis of Variance?
    Reliability in Logistic Models
    But Other Authors Have Argued That Poor Reliability Isn't That Important. Who Is Right?
    Sample Size and the Precision/Stability of Alpha-Empirical CIs
    Summary and Conclusions
    Chapter 17: Prediction in the Generalized Linear Model
    Advance Organizer
    Prediction vs. Explanation
    How is a Prediction Equation Created?
    Shrinkage and Evaluating the Quality of Prediction Equations
    An Example Using Real Data
    Improving on Prediction Models
    Calculating a Predicted Score, and CIs Around That Score
    Prediction (Prognostication) in Logistic Regression (and Other) Models
    An Example of External Validation of a Prognostic Equation Using Real Data
    External Validation of a Prediction Equation
    Using Bootstrap Analysis to Estimate a More Robust Prognostic Equation
    Summary
    Chapter 18: Modeling in Large, Complex Samples: The Importance of Using Appropriate Weights and Design Effect Compensation
    Advance Organizer
    What Types of Studies Use Complex Sampling?
    Why Does Complex Sampling Matter?
    What Are Best Practices in Accounting for Complex Sampling?
    Does It Really Make a Difference in the Results?
    Conditions Used
    Comparison of Unweighted Versus Weighted Analyses
    Summary
    Enrichment