Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded third edition of Fundamentals of Predictive Analytics with JMP bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis.

Using JMP� 17, this book discusses the following new and enhanced features in an example-driven format:

an add-in for Microsoft Excel
Graph Builder
dirty data
visualization
regression
ANOVA
logistic regression
principal component analysis
LASSO
elastic net
cluster analysis
decision trees
k-nearest neighbors
neural networks
bootstrap forests
boosted trees
text mining
association rules
model comparison
time series forecasting

With a new, expansive chapter on time series forecasting and more exercises to test your skills, this third edition is invaluable to those who need to expand their knowledge of statistics and apply real-world, problem-solving analysis.

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