Using classification and regression trees : a practical primer
Discriminant analysis Education Regression analysis Trees (Graph theory) e-böcker
Information Age Publishing Inc.
2018
EISBN 9781641132398
Preface.
Introduction.
Scientific reasoning in the computer age.
Making the case for inductive or data-driven research.
Putting the case in a practical perspective.
Demonstration of CART as an exploratory technique.
Advantages of art.
Notes.
Statistical principles of CART.
Important functions of cart.
Statistical concepts of CART.
Statistical procedures of CART.
Growing the CART tree.
Stopping the cart tree.
Pruning the cart tree.
Notes.
Basic techniques of care.
Statistical techniques of classification trees.
Using costs and priors.
Statistical techniques of regression trees.
Using cost complexity.
Contents.
Using r-squared.
Using surrogates.
Notes.
Issues in cart.
Analysis.
CART versus traditional statistical techniques.
Formulating research questions.
Determining important variables.
Revealing unique variables.
Examining terminal nodes.
Handling missing data.
Determining node size.
Assessing cart performance.
Notes.
Applications of cart.
Operation of CART software programs.
Application 1: Growth in mathematics achievement during middle and high school.
Application 2: Dropping out of advanced mathematics in middle and high school.
Application 3: Science coursework among tenth graders in high school.
Notes.
Advanced techniques of cart.
Extending analytical power of cart.
Concept of hybrid statistical models.
Longitudinal cart analysis.
Multivariate cart analysis.
Multilevel cart analysis.
Cart procedure for meta-analysis.
Concluding statement.
Notes.
References.
A functionally equivalent binary tree.
B common cart software programs.
C spss decision tree syntax.
SPSS decision tree output.
E SPSS decision tree syntax using costs and profits.
F SPSS decision tree syntax using priors.
G SPSS decision tree syntax for drinking and smoking data.
H SPSS decision tree syntax for mental health.
And physical health data.
I SPSS decision tree syntax for cart produce for meta-analysis.
About the author.
Introduction.
Scientific reasoning in the computer age.
Making the case for inductive or data-driven research.
Putting the case in a practical perspective.
Demonstration of CART as an exploratory technique.
Advantages of art.
Notes.
Statistical principles of CART.
Important functions of cart.
Statistical concepts of CART.
Statistical procedures of CART.
Growing the CART tree.
Stopping the cart tree.
Pruning the cart tree.
Notes.
Basic techniques of care.
Statistical techniques of classification trees.
Using costs and priors.
Statistical techniques of regression trees.
Using cost complexity.
Contents.
Using r-squared.
Using surrogates.
Notes.
Issues in cart.
Analysis.
CART versus traditional statistical techniques.
Formulating research questions.
Determining important variables.
Revealing unique variables.
Examining terminal nodes.
Handling missing data.
Determining node size.
Assessing cart performance.
Notes.
Applications of cart.
Operation of CART software programs.
Application 1: Growth in mathematics achievement during middle and high school.
Application 2: Dropping out of advanced mathematics in middle and high school.
Application 3: Science coursework among tenth graders in high school.
Notes.
Advanced techniques of cart.
Extending analytical power of cart.
Concept of hybrid statistical models.
Longitudinal cart analysis.
Multivariate cart analysis.
Multilevel cart analysis.
Cart procedure for meta-analysis.
Concluding statement.
Notes.
References.
A functionally equivalent binary tree.
B common cart software programs.
C spss decision tree syntax.
SPSS decision tree output.
E SPSS decision tree syntax using costs and profits.
F SPSS decision tree syntax using priors.
G SPSS decision tree syntax for drinking and smoking data.
H SPSS decision tree syntax for mental health.
And physical health data.
I SPSS decision tree syntax for cart produce for meta-analysis.
About the author.
