Statistics with JMP: Hypothesis Tests, ANOVA and Regression

Peter Goos, University of Leuven and University of Antwerp, Belgium

David Meintrup, University of Applied Sciences Ingolstadt, Germany

A first course on basic statistical methodology using JMP

This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software.

Key features:

• Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested.
• Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values).
• Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic.
• Promotes the use of graphs and confidence intervals in addition to p-values.
• Course materials and tutorials for teaching are available on the books companion website.

Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.

Statistics with JMP: Hypothesis Tests, ANOVA and Regression

Peter Goos, University of Leuven and University of Antwerp, Belgium

David Meintrup, University of Applied Sciences Ingolstadt, Germany

A first course on basic statistical methodology using JMP

This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software.

Key features:

• Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested.
• Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values).
• Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic.
• Promotes the use of graphs and confidence intervals in addition to p-values.
• Course materials and tutorials for teaching are available on the book's companion website.

Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.