JMP Statistical Thinking for Industrial Problem Solving Practice Exam
JMP Statistical Thinking for Industrial Problem Solving Practice Exam
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