SAS Institute Of Management Studies - Statistical Thinking for Industrial Problem Solving, presented by JMP 

Course Overview

Why You Need a Foundation in Statistical Thinking

First Time Using JMP? View the JMP Quickstart Video

Learner Prerequisites

Taking this Course

Using Forums and Getting Help

Using the JMP Virtual Lab

Introduction

What Is Statistical Thinking?

Overview of Problem Solving

Statistical Problem Solving

Types of Problems

Defining the Problem

Goals and Key Performance Indicators

The White Polymer Case Study

What Is a Process?

Developing a SIPOC Map

Developing an Input/Output Process Map

Top-Down and Deployment Flowcharts

Summary

Tools for Identifying Potential Causes

Brainstorming

Multi-voting

Using Affinity Diagrams

Cause-and-Effect Diagrams

The 5 Whys

Cause-and-Effect Matrices

Summary

Data Collection for Problem Solving

Types of Data

Operational Definitions

Data Collection Strategies

Importing Data for Analysis

Activity: Developing a Cause-and-Effect Diagram

Read About It

Summary: Statistical Thinking and Problem Solving

Question 1.01

Question 1.03

Question 1.04

Question 1.06

Question 1.07

Question 1.08

Question 1.09

Question 1.10

Question 1.12

Question 1.13

Question 1.15

Question 1.16

Questions 1.18 - 1.19

Question 1.20

Question 1.21

Questions 1.23-1.25

Introduction

Introduction to Descriptive Statistics

Types of Data

Histograms

Demo: Creating Histograms in JMP

Demo: Saving Your Work Using Scripts

The Chemical Manufacturing Case Study

The White Polymer Case Study

Measures of Central Tendency and Location

Demo: Summarizing Continuous Data with the Distribution Platform

Demo: Summarizing Continuous Data with Column Viewer and Tabulate

Measures of Spread: Range and Interquartile Range

Demo: Hiding and Excluding Data

Measures of Spread: Variance and Standard Deviation

Visualizing Continuous Data

Demo: Creating Tabular Summaries with Tabulate

Demo: Creating Scatterplots and Scatterplot Matrices

Demo: Creating Comparative Box Plots with Graph Builder

Demo: Creating Run Charts (Line Graphs) with Graph Builder

Describing Categorical Data

Creating Tabular Summaries for Categorical Data

Demo: Creating Bar Charts and Mosaic Plots

Review and Introduction to Probability Concepts

Samples and Populations

Understanding the Normal Distribution

Checking for Normality

Demo: Checking for Normality

Demo: Finding the Area Under a Curve

The Central Limit Theorem

Demo: Exploring the Central Limit Theorem

Introduction to Exploratory Data Analysis

Exploring Continuous Data: Enhanced Tools

Demo: Adding Markers, Colors, and Row Legends

Demo: Switching Columns in an Analysis

Pareto Plots

Demo: Creating Sorted Bar Charts and Pareto Plots

Packed Bar Charts and Data Filtering

Demo: Creating Packed Bar Charts

Demo: Using the Local Data Filter

Tree Maps and Mosaic Plots

Demo: Creating a Tree Map

Using Trellis Plots and Overlay Variables

Demo: Creating Trellis Plots and Using Overlay Variables

Bubble Plots and Heat Maps

Demo: Creating Bubble Plots

Demo: Creating Heat Maps

Visualizing Geographic and Spatial Data

Demo: Creating a Geographic Map Using Shape Files

Demo: Creating Maps Using Coordinates

Summary of Exploratory Data Analysis Tools

Question 2.01

Question 2.02

Practice: Understanding Yield for a Chemical Manufacturing Process

Practice: Exploring the Relationship Between Variables

Question 2.03 - 2.04

Practice: Summarizing Continuous Data with the Distribution Platform

Question 2.06 - 2.07

Practice: Understanding Box Plots

Question 2.08

Question 2.09

Practice: Visualizing Continuous Data

Question 2.10 - 2.11

Practice: Visualizing Categorical Data

Question 2.13

Question 2.15

Practice: Checking for Normality

Practice: Recognizing Shapes in Normal Quantile Plots

Practice: Exploring the Central Limit Theorem

Question 2.16

Practice: Exploring Many Variables Using the Column Switcher

Question 2.17 - 2.18

Practice: Creating Sorted Bar Charts in JMP

Question 2.19

Practice: Exploring Data with a Local Data Filter

Question 2.20

Practice: Exploring Data with a Tree Map and Mosaic Plot

Practice: Exploring Data Using Trellis Plots

Question 2.21

Practice: Exploring Data Using Bubble Plots and Heat Maps

Question 2.22

Practice: Exploring Data with a Geographic Map

Introduction to Communicating with Data

Creating Effective Visualizations

Evaluating the Effectiveness of a Visualization

Designing an Effective Visualization: Part 1

Designing an Effective Visualization: Part 2

Communicating Visually with Animation

Designing for Your Audience

Understanding Your Target Audience

Designing Visualizations for Communication

Designing Visualizations: The Do's

Designing Visualizations: The Don'ts

Demo: Customizing Graphics

Introduction to Saving and Sharing Results

Saving and Sharing Results in JMP

Saving and Sharing Results outside of JMP

Deciding Which Format to Use

Demo: Organizing Your Saved Scripts

Demo: Combining JMP Scripts for Analyses

Demo: Sharing Static Output

Demo: Saving Your Work in a JMP Journal

Data Tables Essentials

Common Data Quality Issues

Identifying Issues in the Data Table

Identifying Issues One Variable at a Time

Summarizing What You Have Learned

Demo: Exploring Missing Values

Demo: Using Recode

Restructuring Data for Analysis

Demo: Stacking and Splitting Data

Combining Data

Demo: Concatenating Data Tables

Demo: Joining Data Tables

Deriving New Variables

Demo: Binning Data Using Conditional IF-THEN Statements

Demo: Transforming Data

Working with Dates

Read About It

Summary - Exploratory Data Analysis

Question 2.24

Question 2.25

Question 2.26

Question 2.28 - 2.29

Practice: Customizing Graphics

Practice: Creating a Slope Graph

Question 2.31 - 2.32

Question 2.33

Practice: Exploring Reports Published on JMP Public

Practice: Grouping and Combining Analysis Scripts

Practice: Creating a Simple Dashboard

Practice: Using a JMP Journal to Document Your Work

Question 2.34

Question 2.35

Practice: Creating the Formula for Scrap Rate

Practice: Checking the Data Table for Issues

Question 2.36

Practice: Checking Data Quality with Summary Statistics and Graphs

Question 2.37 - 2.38

Question 2.39

Practice: Exploring Missing Data

Practice: Recoding Missing Values

Practice: Using Recode to Bin Data

Question 2.40

Practice: Stacking Data

Question 2.41

Practice: Concatenating Data Tables

Practice: Joining Data Tables

Practice: Creating a Binning Formula

Practice: Extracting Information from a Column

Practice: Working with Dates

Introduction

Quality Methods Overview

Introduction to Control Charts

Individual and Moving Range Charts

Demo: Creating an I and MR Chart Using the Control Chart Builder

Common Cause versus Special Cause Variation

Testing for Special Causes

Demo: Testing for Special Causes in the Control Chart Builder

X-bar and R and X-bar and S Charts

Demo: Creating X-bar and R and X-bar and S Charts

Rational Subgrouping

3-Way Control Charts

Demo: Creating 3-Way Control Charts

Control Charts with Phases

Demo: Adding Phases to Control Charts

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

Demo: Calculating Capability Indices Using the Distribution Platform

Demo: Conducting a Capability Analysis Using the Control Chart Builder

Calculating Capability for Nonnormal Data

Demo: Estimating Capability for Nonnormal Data

Estimating Process Capability for Many Variables

Identifying Poorly Performing Processes

Demo: Identifying Poorly Performing Processes

A View from Industry

What is a Measurement Systems Analysis

Language and Terminology

Designing a Measurement System Study

Designing and Conducting an MSA

Demo: Creating a Gauge Study Worksheet

Analyzing an MSA with Visualizations

Demo: Visualizing Measurement System Variation

Analyzing the MSA

Demo: Analyzing an MSA

Demo: Conducting a Gauge R&R Analysis

Studying Measurement System Accuracy

Demo: Analyzing Measurement System Bias

Improving the Measurement Process

Activity: Area MSA

Read About It

Summary: Quality Methods

Question 3.02

Practice: Creating an I and MR Chart

Question 3.03

Question 3.04

Practice: Creating I and MR Charts for the White Polymer Case Study

Practice: Constructing an X-Bar and S Chart

Question 3.05

Question 3.06

Practice: Evaluating whether Improvements Have Been Sustained

Practice: Using Control Charts as an Exploratory Tool

Question 3.07

Question 3.08

Activity: Calculating Capability Indices

Question 3.09

Question 3.10 - 3.11

Practice: Calculating Capability Indices

Practice: Conducting a Capability Analysis with a Phase Variable

Practice: Conducting a Capability Analysis with Nonnormal Data

Question 3.12

Question 3.13

Practice: Designing a Gauge Study

Practice: Visualizing the Area Measurement MSA Data

Practice: Visualizing the MFI MSA Data

Practice: Analyze the Area Measurement MSA Data

Practice: Analyzing the Melt Flow Index MSA

Question 3.15

Introduction to Decision Making with Data

Introduction to Statistical Inference

What Is a Confidence Interval?

A Practical Example

Estimating a Mean

Visualizing Sampling Variation

Constructing Confidence Intervals

Demo: Understanding the Confidence Level and Alpha Risk

Demo: Calculating Confidence Intervals

Prediction Intervals

Tolerance Intervals

Demo: Calculating Prediction and Tolerance Intervals

Comparing Interval Estimates

Introduction to Statistical Testing

Statistical Decision Making

Understanding the Null and Alternative Hypothesis

Sampling Distribution under the Null

The p-Value and Statistical Significance

Summary of Foundations in Statistical Testing

Conducting a One-Sample t Test

Demo: Conducting a One-Sample t Test

Demo: Understanding p-Values and t Ratios

Equivalence Testing

Comparing Two Means

Two-Sample t Tests

Unequal Variances Tests

Demo: Conducting a Two-Sample t Test

Paired Observations

Demo: Performing a Paired t Test

Comparing More Than Two Means

One-Way ANOVA (Analysis of Variance)

Multiple Comparisons

Demo: Comparing More Than Two Means

Revisiting Statistical Versus Practical Significance

Summary of Hypothesis Testing for Continuous Data

Introduction to Sample Size and Power

Sample Size for a Confidence Interval for the Mean

Demo: Calculating the Sample Size for a Confidence Interval

Outcomes of Statistical Tests

Statistical Power

Exploring Sample Size and Power

Demo: Exploring the Power Animation

Calculating the Sample Size for One-Sample t Tests

Demo: Calculating the Sample Size for a One-Sample t Test

Calculating the Sample Size for Two-Sample t Tests

Demo: Calculating the Sample Size for Two or More Sample Means

Summary of Sample Size and Power

Read About It

Summary: Decision Making with Data

Question 4.01

Question 4.02

Question 4.03

Questions 4.04 - 4.06

Practice: Constructing a Confidence Interval

Practice: Comparing Intervals at Different Confidence Levels

Practice: Constructing a Confidence Interval for the Speed of Light

Question 4.07

Question 4.08

Practice: Constructing Prediction and Tolerance Intervals

Question 4.09

Practice: Comparing Interval Estimates

Question 4.11

Questions 4.12 - 4.14

Question 4.15

Questions 4.16 - 4.18

Question 4.20

Practice: Conducting a One-Sample t Test

Practice: Conducting a One-Sample t Test with a BY Variable

Practice: Conducting an Equivalence Test

Question 4.21

Practice: Conducting a Two-Sample t Test

Practice: Conducting an Equivalence Test for Two Means

Practice: Conducting an Unequal Variances Test

Question 4.22

Practice: Conducting a Paired t Test

Question 4.23

Practice: Conducting a One-Way ANOVA Analysis

Practice: Comparing Several Means

Question 4.25

Question 4.26

Practice: Calculating Sample Size for a CI for a Mean

Practice: Calculating Sample Size for a CI for a Proportion

Question 4.27 - 4.28

Question 4.30

Question 4.31

Practice: Calculating Sample Size for a One-Sample t Test

Practice: Calculating Sample Size for a Two-Sample t Test

Introduction

What Is Correlation?

Interpreting Correlation

Demo: Exploring the Impact of Outliers on Correlation

Demo: Assessing Correlations

Introduction to Regression Analysis

Demo: Fitting a Regression Model

The Simple Linear Regression Model

The Method of Least Squares

Demo: The Method of Least Squares

Visualizing the Method of Least Squares

Regression Model Assumptions

Demo: Evaluating Model Assumptions

Interpreting Regression Results

Demo: Interpreting Regression Analysis Results

Fitting a Model with Curvature

Demo: Fitting Polynomial Models

What is Multiple Linear Regression?

Fitting the Multiple Linear Regression Model

Demo: Fitting Multiple Linear Regression Models

Interpreting Results in Explanatory Modeling

Demo: Using the Prediction Profiler

Residual Analysis and Outliers

Demo: Analyzing Residuals and Outliers

Multiple Linear Regression with Categorical Predictors

Demo: Fitting a Model with Categorical Predictors

Multiple Linear Regression with Interactions

Demo: Fitting a Model with Interactions

Variable Selection

Demo: Selecting Variables Using Effect Summary

Multicollinearity

Demo: Assessing Multicollinearity

Closing Thoughts on Multiple Linear Regression

What Is Logistic Regression?

The Simple Logistic Model

Simple Logistic Regression Example

Interpreting Logistic Regression Results

Demo: Fitting a Simple Logistic Regression Model

Multiple Logistic Regression

Demo: Fitting a Multiple Logistic Regression Model

Logistic Regression with Interactions

Demo: Fitting a Logistic Regression Model with Interactions

Common Issues

Read About It

Summary: Correlation and Regression

Question 5.01

Question 5.02-5.03

Practice: Exploring Correlations (Example)

Practice: Exploring Correlations (Case Study)

Question 5.05

Practice: Fitting a Simple Linear Regression Model

Question 5.06

Practice: Exploring Least Squares

Practice: Visualizing Regression with Anscombe's Quartet

Practice: Interpreting Regression Analysis Results

Practice: Fitting Polynomial Models

Question 5.08

Practice: Comparing Simple Linear and Multiple Linear Regression Models

Question 5.09

Practice: Exploring Significant Predictors

Question 5.10

Practice: Identifying Outliers and Influential Observations

Question 5.11

Practice: Fitting a Model with Categorical Predictors

Question 5.12

Practice: Fitting a Model with Interactions

Practice: Selecting Variables Using Effect Summary

Question 5.14

Question 5.15

Practice: Regression Modeling Mini Case Study

Question 5.16

Question 5.17

Practice: Fitting a Simple Logistic Model for Reaction Time

Practice: Fitting a Multiple Logistic Regression Model

Practice: Fitting a Logistic Regression Model with Interactions

Introduction

A View from Industry

What is DOE?

Conducting Ad Hoc and One-Factor-at-a-Time (OFAT) Experiments

Why Use DOE?

Terminology of DOE

Types of Experimental Designs

Designing Factorial Experiments

Demo: Designing Full Factorial Experiments

Analyzing a Replicated Full Factorial

Analyzing an Unreplicated Full Factorial

Demo: Analyzing Full Factorial Experiments

Summary of Factorial Experiments

Screening for Important Effects

A Look at Fractional Factorial Designs

Demo: Creating 2^k-r Fractional Factorial Designs

Custom Screening Designs

Demo: Creating Screening Designs in the Custom Designer

Introduction to Response Surface Designs

Response Surface Designs for Two Factors

Analyzing Response Surface Experiments

Demo: Designing a Central Composite Design

Creating Custom Response Surface Designs

Sequential Experimentation

Response Surface Summary

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

The Anodize Case Study: Part 1

The Anodize Case Study: Part 2

Summary

Demo: Optimizing Multiple Responses

Demo: Simulating Data Using the Prediction Profiler

Read About It

Summary: Design of Experiments (DOE)

Question 6.01 - 6.02

Question 6.03

Question 6.04

Question 6.05

Question 6.06 - 6.07

Question 6.08

Question 6.09 - 6.12

Practice: Designing a Full Factorial Experiment

Question 6.13 - 6.14

Question 6.15

Question 6.16

Practice: Analyzing a Replicated Full Factorial Experiment

Question 6.17

Question 6.18 - 6.19

Practice: Designing a Fractional Factorial Experiment

Practice: Analyzing a 20-Run Custom Design

Question 6.21- 6.22

Question 6.23 - 6.24

Practice: Analyzing a Custom Central Composite Design

Practice: Optimizing the Heck Reaction

Question 6.26

Question 6.27 - 6.28

Question 6.29

Question 6.30

Practice: Optimizing Multiple Responses

Introduction

Introduction to Predictive Modeling

Overfitting and Model Validation

Demo: Creating a Validation Column

Assessing Model Performance: Prediction Models

Demo: Fitting a Multiple Linear Regression Model with Validation

Assessing Model Performance: Classification Models

Receiver-Operating Characteristic (ROC) Curves

Demo: Fitting a Logistic Model with Validation

Demo: Changing the Cutoff for Classification

Introduction to Decision Trees

Classification Trees

Demo: Creating a Classification Tree

Regression Trees

Demo: Fitting a Regression Tree

Decision Trees with Validation

Demo: Fitting a Decision Tree with Validation

Random (Bootstrap) Forests

Demo: Variable Selection with a Bootstrap Forest

What is a Neural Network?

Interpreting Neural Networks

Demo: Fitting a Neural Network

Predictive Modeling with Neural Networks

Demo: Fitting a Neural Model with Two Layers

Introduction to Generalized Regression

Fitting Models Using Maximum Likelihood

Demo: Fitting a Linear Model in Generalized Regression

Demo: Variable Selection in Generalized Regression

Introduction to Penalized Regression

Demo: Fitting a Penalized Regression (Lasso) Model

Comparing Predictive Models

Demo: Comparing and Selecting Predictive Models

Introduction to Text Mining

Processing Text Data

Curating the Term List

Demo: Processing Unstructured Text Data

Visualizing and Exploring Text Data

Demo: Visualizing and Exploring Text Data

Analyzing (Mining) Text Data

Read About It

Summary: Predictive Modeling and Text Mining

Question 7.01

Question 7.02

Question 7.03

Practice: Fitting a Multiple Linear Regression Model with Validation

Practice: Fitting a Logistic Model with Validation

Question 7.04

Practice: Using a Classification Tree for Problem Solving

Practice: Identifying Important Variables

Question 7.05

Question 7.06

Practice: Using a Regression Tree with Validation

Practice: Using a Classification Tree with Validation

Question 7.07

Practice: Using Trees to Identify Important Variables

Question 7.08

Practice: Fitting a Simple Neural Network

Practice: Fitting a Neural Network for Prediction

Practice: Fitting a Neural Network for Classification

Question 7.09

Question 7.10

Question 7.11 - 7.12

Practice: Reducing a Model Using Generalized Regression

Practice: Fitting a Regression Model using the Lasso

Question 7.13

Practice: Comparing and Selecting Predictive Models

Question 7.14

Question 7.15

Question 7.16

Practice: Developing a Term List

Practice: Exploring Terms and Phrases in STIPS

Review Questions

Case Studies