Statistical Thinking for Industrial Problem Solving

A free online statistics course

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.

Explore topics by module (or download a PDF version):

Statistical Thinking and Problem Solving

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
Exploratory Data 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 for Your Audience
  • Understanding Your Target Audience
  • 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
Quality Methods

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
Decision Making With Data

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 and Regression

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
Design of Experiments

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
Predictive Modeling and Text Mining

Essentials of Predictive Modeling

  • Introduction to Predictive Modeling
  • Overfitting and Model Validation
  • Assessing Model Performance: Prediction Models
  • Assessing Model Performance: Classification Models
  • Receiver-Operating Characteristic (ROC) Curves

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

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