Practical Data Analysis with JMP, Third Edition

  • 7h 6m
  • Robert H. Carver
  • SAS Institute
  • 2019

Master the concepts and techniques of statistical analysis using JMP

Practical Data Analysis with JMP, Third Edition, highlights the powerful interactive and visual approach of JMP to introduce readers to statistical thinking and data analysis. It helps you choose the best technique for the problem at hand by using real-world cases. It also illustrates best-practice workflow throughout the entire investigative cycle, from asking valuable questions through data acquisition, preparation, analysis, interpretation, and communication of findings.

The book can stand on its own as a learning resource for professionals, or it can be used to supplement a college-level textbook for an introductory statistics course. It includes varied examples and problems using real sets of data. Each chapter typically starts with an important or interesting research question that an investigator has pursued. Reflecting the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, and economics, as well as international and historical examples.

Application Scenarios at the end of each chapter challenge you to use your knowledge and skills with data sets that go beyond mere repetition of chapter examples.

New in the third edition, chapters have been updated to demonstrate the enhanced capabilities of JMP, including projects, Graph Builder, Query Builder, and Formula Depot.

In this Book

  • About This Book
  • Getting Started—Data Analysis with JMP
  • Data Sources and Structures
  • Describing a Single Variable
  • Describing Two Variables at a Time
  • Review of Descriptive Statistics
  • Elementary Probability and Discrete Distributions
  • The Normal Model
  • Sampling and Sampling Distributions
  • Review of Probability and Probabilistic Sampling
  • Inference for a Single Categorical Variable
  • Inference for a Single Continuous Variable
  • Chi-Square Tests
  • Two-Sample Inference for a Continuous Variable
  • Analysis of Variance
  • Simple Linear Regression Inference
  • Residuals Analysis and Estimation
  • Review of Univariate and Bivariate Inference
  • Multiple Regression
  • Categorical, Curvilinear, and Non-Linear Regression Models
  • Basic Forecasting Techniques
  • Elements of Experimental Design
  • Quality Improvement
  • Bibliography
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