15: Simple Linear Regression Inference
Overview
Fitting a Line to Bivariate Continuous Data
The Simple Regression Model
What Are We Assuming?
Interpreting Regression Results
Application
Chapter 16: Residuals Analysis and Estimation
Overview
Conditions for Least Squares Estimation
Residuals Analysis
Estimation
Application
Chapter 17: Review of Univariate and Bivariate Inference
Overview
Research Context
One Variable at a Time
Life Expectancy by Income Group
Life Expectancy by GDP per Capita
Conclusion
Chapter 18: Multiple Regression
Overview
The Multiple Regression Model
Visualizing Multiple Regression
Fitting a Model
A More Complex Model
Residuals Analysis in the Fit Model Platform
Using a Regression Tree Approach: The Partition Platform
Collinearity
Evaluating Alternative Models
Application
Chapter 19: Categorical, Curvilinear, and Non-Linear Regression Models
Overview
Dichotomous Independent Variables
Dichotomous Dependent Variable
Curvilinear and Non-Linear Relationships
More Non-Linear Functions
Application
Chapter 20: Basic Forecasting Techniques
Overview
Detecting Patterns Over Time
Smoothing Methods
Trend Analysis
Autoregressive Models
Application
Chapter 21: Elements of Experimental Design
Overview
Why Experiment?
Goals of Experimental Design
Factors, Blocks, and Randomization
Multi-Factor Experiments and Factorial Designs
Blocking
A Design for Main Effects Only
Definitive Screening Designs
Non-Linear Response Surface Designs
Application
Chapter 22: Quality Improvement
Overview
Processes and Variation
Control Charts
Variability Charts
Capability Analysis
Pareto Charts
Application
About This Book
What Does This Book Cover?
Purpose: Learning to Reason Statistically
We live in a world of uncertainty. Today more than ever before, we have vast resources of data available to shed light on crucial questions. But at the same time, the sheer volume and complexity of the “data deluge” can distract and overwhelm us. The goal of applied statistical analysis is to work with data to calibrate, cope with, and sometimes reduce uncertainty. Business decisions, public policies, scientific research, and news reporting are all shaped by statistical analysis and reasoning. Statistical thinking is an essential part of the boom in “big data analytics” in numerous professions. This book will help you use and discriminate among some fundamental techniques of analysis, and it will also help you engage in statistical thinking by analyzing real problems. You will come to see statistical investigations as an iterative process and will gain experience in the major phases of that process.
To be an effective analyst or consumer of other people’s analyses, you must know how to use these techniques, when to use them, and how to communicate their implications. Knowing how to use these techniques involves mastery of computer software like JMP. Knowing when to use these techniques requires an understanding of the theory underlying the techniques and practice with applications of the theory. Knowing how to effectively communicate with consumers of an analysis or with other analysts requires a clear understanding of the theory and techniques, as well as clarity of expression, directed toward one’s audience.
There was a time when a first course in statistics emphasized abstract theory, laborious computation, and small sets of artificial data—but not practical data analysis or interpretation. Those days are thankfully past, and now we can address all three of the skill sets just cited.
Scope and Structure of This Book
As a discipline, statistics is large and growing; the same is true of JMP. One paperback book must limit its scope, and the content boundaries of this book are set intentionally along several dimensions.
First, this book provides considerable training in the basic functions of JMP 15. JMP is a full-featured, highly interactive, visual, and comprehensive package. The book assumes that you have the software at your school or office. The software’s capabilities extend far beyond an introductory course, and this book makes no attempt to “cover” the entire program. The book introduces students to its major platforms and essential features and should leave students with sufficient background and confidence to continue exploring on their own. Fortunately, the Help system and accompanying manuals are quite extensive, as are the learning resources available online at http://www.jmp.com.
Second, the chapters largely follow a traditional sequence, making the book compatible with many current texts. As such, instructors and students will find it easy to use the book as a companion volume in an introductory course. Chapters are organized around core statistical concepts rather