2016 INFORMS International The Analytics Tool Kit: A Case Study with JMP Pro

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Vincent Evans
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2 Background TQM Coordinator/Six Sigma MBB Founding Partner, Statistical Trainer and Consultant, North Haven Group Senior Consultant and Trainer, George Group (now Accenture) Adjunct Professor Statistics, University of New Hampshire Academic Ambassador, JMP Academic Team Author 2

3 Background 3

doing Comprehensive Basic Advanced Extendible powerful scripting language

4 What is JMP (and JMP Pro)? Statistical Discovery Software from SAS Developed in 1989 For teaching and doing Comprehensive Basic Advanced Extendible powerful scripting language application and add-in builders Excel, R, MATLAB, and SAS Visual, dynamic and interactive JMP Pro Advanced tools for analytics and modeling 4

5 What is Analytics? an encompassing and multidimensional field that uses mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns and knowledge in recorded data. Descriptive statistics Predictive analytics Prescriptive analytics From SAS Analytics: 5

6 What is Analytics? Descriptive statistics Understand what happened and why Exploratory and explanatory modeling Predictive analytics Using past data and predictive algorithms to determine what will happen next Predictive modeling Prescriptive analytics Answering the question of what to do, by providing information on optimal decisions based on the predicted future scenarios Simulation and optimization 6

The Business Analytics Process Define the

Modeling Deploy Model From Building Better

7 The Business Analytics Process Define the Problem May loop back at any step Business Problem Prepare for Modeling Business Analytics Process Monitor Performance Modeling Deploy Model From Building Better Models with JMP Pro, Grayson, Gardner and Stephens,

8 Data Preparation: Activities and Tools Key Activities: Determine which data are needed Compile (or collect new) data Explore, examine and understand data Assess data quality Clean and transform data Define features Reduce dimensionality Create training, validation and test sets Key Tools: SQL/Query Data table structuring - join, concatenate, update, stack, summarize, Summary statistics and graphical displays, interactive tools and filtering Multivariate procedures (clustering, PCA, ) Transformations, creating derived variables Missing data utilities, outlier analysis, recoding, binning Creating holdout set(s) 8

9 Modeling: Activities and Tools Key Activities: Choose the appropriate modeling method or methods Fit one or more models Evaluate the performance of each model using validation statistics (misclassification, RMSE, Rsquare) Choose the best model or set of models to address the analytics problem (and ultimately the business problem) **Create ensemble models Key Tools: Multiple Regression Logistic Regression Naïve Bayes knn Classification and Regression Trees Bootstrap Forests and Boosted Trees Neural Networks Generalized Linear Models Survival Models Forecasting/Time Series Model Comparison Text Mining 9

10 Deploy Models: Activities and Tools Key Activities and Tools: Define the Problem May loop back at any step Deliver the model and model results to the business or internal customers Business Problem Monitor Performance Business Analytics Process Prepare for Modeling Modeling Communicate results (graphs and profilers, summaries, explore what if scenarios) Assist in applying model insights and implementing ongoing use of the model Deploy Model Document the project Follow up with the business sponsor to close out the project 10

Case Study: Property Values on the Big Island 11

12 Case Study: Property Values on the Big Island Data from Redfin.com. Downloaded June 6,

13 The Business Analytics Process Define the Problem May loop back at any step Business Problem Prepare for Modeling Business Analytics Process Monitor Performance Modeling Deploy Model From Building Better Models with JMP Pro, Grayson, Gardner and Stephens,

14 For more information jmp.com/academic Learning library (guides and videos) Case studies Tools for teaching statistical concepts Books for teaching with JMP jmp.com/jac Discussion forum File exchange More teaching and learning resources This slide deck and data set 14

Discussion and Q&A Mia.stephens@jmp.com jmp.

DASI: Analytics in Practice and Academic Analytics Preparation

DASI: Analytics in Practice and Academic Analytics Preparation Mia Stephens mia.stephens@jmp.com Copyright 2010 SAS Institute Inc. All rights reserved. Background TQM Coordinator/Six Sigma MBB Founding