Course Outline

The Statistical Thinking for Industrial Problem Solving course is comprised of seven modules, totaling about 30 hours of self-paced learning. Each module includes short instructional videos, JMP demonstrations, questions and exercises. The topics covered in each module are outlined below.

Statistical Thinking

• What is Statistical Thinking

Problem Solving

• Overview of Problem Solving
• Statistical Problem Solving
• Types of Problems

Defining the Problem

• Defining the Problem
• Goals and Key Performance Indicators
• The White Polymer Case Study

Defining the Process

• What is a Process?
• Developing a SIPOC Map
• Developing an Input/Output Process Map
• Top-Down and Deployment Flowcharts

Identifying Potential Root Causes

• Tools for Identifying Potential Causes
• Brainstorming
• Multi-voting
• Using Affinity Diagrams
• Cause-and-Effect Diagrams
• The Five Whys
• Cause-and-Effect Matrices

Compiling and Collecting Data

• Data Collection for Problem Solving
• Types of Data
• Operational Definitions
• Data Collection Strategies
• Importing Data for Analysis

Describing Data

• Introduction to Descriptive Statistics
• Types of Data
• Histograms
• Measures of Central Tendency and Location
• Measures of Spread — Range and Interquartile Range
• Measures of Spread — Variance and Standard Deviation
• Visualizing Continuous Data
• Describing Categorical Data

Probability Concepts

• Introduction to Probability Concepts
• Samples and Populations
• Understanding the Normal Distribution
• Checking for Normality
• The Central Limit Theorem

Exploratory Data Analysis for Problem Solving

• Introduction to Exploratory Data Analysis
• Exploring Continuous Data: Enhanced Tools
• Pareto Plots
• Packed Bar Charts and Data Filtering
• Tree Maps and Mosaic Plots
• Using Trellis Plots and Overlay Variables
• Bubble Plots and Heat Maps
• Summary of Exploratory Data Analysis Tools

Communicating with Data

• Introduction to Communicating with Data
• Creating Effective Visualizations
• Evaluating the Effectiveness of a Visualization
• Designing an Effective Visualization
• Communicating Visually with Animation
• Designing Visualizations for Communication
• Designing Visualizations: The Do's and Don'ts

Saving and Sharing Results

• Introduction to Saving and Sharing Results
• Saving and Sharing Results in JMP
• Saving and Sharing Results Outside of JMP
• Deciding Which Format to Use

Data Preparation for Analysis

• Data Tables Essentials
• Common Data Quality Issues
• Identifying Issues in the Data Table
• Identifying Issues One Variable at a Time
• Restructuring Data for Analysis
• Combining Data
• Deriving New Variables
• Working with Dates

Statistical Process Control

• Introduction to Control Charts
• Individual and Moving Range Charts
• Common Cause versus Special Cause Variation
• Testing for Special Causes
• X-bar and R, and X-bar and S Charts
• Rational Subgrouping
• 3-Way Control Charts
• Control Charts with Phases

Process Capability

• The Voice of the Customer
• Process Capability Indices
• Short- and Long-Term Estimates of Capability
• Understanding Capability for Process Improvement
• Estimating Process Capability: An Example
• Calculating Capability for Nonnormal Data
• Estimating Process Capability for Many Variables
• Identifying Poorly Performing Processes
• A View from Industry

Measurement System Studies

• What is a Measurement Systems Analysis (MSA)?
• Language and Terminology
• Designing a Measurement System Study
• Designing and Conducting an MSA
• Analyzing an MSA
• Studying Measurement System Accuracy
• Improving the Measurement Process

Estimation

• Introduction to Statistical Inference
• What Is a Confidence Interval?
• Estimating a Mean
• Visualizing Sampling Variation
• Constructing Confidence Intervals
• Understanding the Confidence Level and Alpha Risk
• Prediction Intervals
• Tolerance Intervals
• Comparing Interval Estimates

Foundations in Statistical Testing

• Introduction to Statistical Testing
• Statistical Decision-Making
• Understanding the Null and Alternative Hypotheses
• Sampling Distribution under the Null
• The p-Value and Statistical Significance

Hypothesis Testing for Continuous Data

• Conducting a One-Sample t Test
• Understanding p-Values and t Ratios
• Equivalence Testing
• Comparing Two Means
• Unequal Variances Tests
• Paired Observations
• One-Way ANOVA (Analysis of Variance)
• Multiple Comparisons
• Statistical Versus Practical Significance

Sample Size and Power

• Introduction to Sample Size and Power
• Sample Size for a Confidence Interval for the Mean
• Outcomes of Statistical Tests
• Statistical Power
• Exploring Sample Size and Power
• Calculating the Sample Size for One-Sample t Tests
• Calculating the Sample Size for Two-Sample t Tests and ANOVA

Correlation

• What is Correlation?
• Interpreting Correlation

Simple Linear Regression

• Introduction to Regression Analysis
• The Simple Linear Regression Model
• The Method of Least Squares
• Visualizing the Method of Least Squares
• Regression Model Assumptions
• Interpreting Regression Results
• Fitting a Model with Curvature

Multiple Linear Regression

• What is Multiple Linear Regression?
• Fitting the Multiple Linear Regression Model
• Interpreting Results in Explanatory Modeling
• Residual Analysis and Outliers
• Multiple Linear Regression with Categorical Predictors
• Multiple Linear Regression with Interactions
• Variable Selection
• Multicollinearity

Introduction to Logistic Regression

• What Is Logistic Regression?
• The Simple Logistic Model
• Simple Logistic Regression Example
• Interpreting Logistic Regression Results
• Multiple Logistic Regression
• Logistic Regression with Interactions
• Common Issues

Introduction to DOE

• What is DOE?
• Conducting Ad Hoc and One-Factor-at-a-Time (OFAT) Experiments
• Why Use DOE?
• Terminology of DOE
• Types of Experimental Designs

Factorial Experiments

• Designing Factorial Experiments
• Analyzing a Replicated Full Factorial
• Analyzing an Unreplicated Full Factorial

Screening Experiments

• Screening for Important Effects
• A Look at Fractional Factorial Designs
• Custom Screening Designs

Response Surface Experiments

• Introduction to Response Surface Designs
• Analyzing Response Surface Experiments
• Creating Custom Response Surface Designs
• Sequential Experimentation

DOE Guidelines

• Introduction to DOE Guidelines
• Defining the Problem and the Objectives
• Identifying the Responses
• Identifying the Factors and Factor Levels
• Identifying Restrictions and Constraints
• Preparing to Conduct the Experiment
• Case Study

Essentials of Predictive Modeling

• Introduction to Predictive Modeling
• Overfitting and Model Validation
• Assessing Model Performance: Prediction Models
• Assessing Model Performance: Classification Models

Decision Trees

• Introduction to Decision Trees
• Classification Trees
• Regression Trees
• Decision Trees with Validation
• Random (Bootstrap) Forests

Neural Networks

• What is a Neural Network?
• Interpreting Neural Networks
• Predictive Modeling with Neural Networks

Generalized Regression

• Introduction to Generalized Regression
• Fitting Models Using Maximum Likelihood
• Introduction to Penalized Regression

Model Comparison and Selection

• Comparing Predictive Models

Introduction to Text Mining

• Introduction to Text Mining
• Processing Text Data
• Curating the Term List
• Visualizing and Exploring Text Data
• Analyzing (Mining) Text Data