JMP Statistical Thinking for Industrial Problem Solving Practice Exam

The JMP Statistical Thinking for Industrial Problem Solving course is designed for individuals aiming to enhance their proficiency in practical problem-solving utilizing data and statistics. It covers the content areas and tasks of the Statistical Thinking for Industrial Problem Solving certification exam.

This e-learning course, spanning 25 to 30 hours, incorporates mini-lectures, case studies, demonstrations, practices, and quizzes, ensuring an interactive and applied learning experience. The course is divided into seven standalone modules, each requiring three to five hours for completion. Participants gain access to JMP software in a virtual lab, along with the JMP Start Statistics eBook and supplementary resources.

Key Learning Objectives include:

• Process mapping, project scoping, and defining data requirements for problem-solving.
  
• Utilizing graphical representations and interactive visualizations to interpret and communicate data insights effectively.
• Implementing tools for quantifying, controlling, and minimizing variation in products, services, or processes.
• Drawing conclusions from data through statistical intervals, hypothesis testing, and understanding sample size and power relationships.
• Exploring linear associations between variables and interpreting linear and logistic regression models.
• Familiarizing with the terminology and principles of design of experiments (DOE).
• Planning, executing, and analyzing experiments using JMP.
• Identifying potential relationships, building predictive models, and extracting insights from unstructured text data.

Prerequisite:

• While familiarity with JMP software is beneficial, it is not mandatory for participation in this course.
• This course predominantly focuses on JMP software applications.

Course Outline 

This covers the given topics  - 

Topic 1: Understand 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

Topic 2: Learn about 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

Topic 3: Explore 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

Topic 4: 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

Topic 5: Understand 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

Topic 6: Understand 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

Topic 7: 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