Statistical Thinking for Industrial Problem Solving, presented by JMP

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Approx. 44 hours to complete
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Course Summary

This course focuses on the practical application of statistical thinking in real-world scenarios. Students will learn how to collect, analyze, and interpret data, as well as make informed decisions based on that data.

Key Learning Points

  • Understand the importance of statistical thinking in decision-making
  • Learn how to collect and analyze data using statistical methods
  • Apply statistical thinking to real-world scenarios and make informed decisions

Job Positions & Salaries of people who have taken this course might have

    • USA: $62,453
    • India: ₹543,799
    • Spain: €31,502
    • USA: $62,453
    • India: ₹543,799
    • Spain: €31,502

    • USA: $69,160
    • India: ₹762,079
    • Spain: €36,509
    • USA: $62,453
    • India: ₹543,799
    • Spain: €31,502

    • USA: $69,160
    • India: ₹762,079
    • Spain: €36,509

    • USA: $58,000
    • India: ₹470,000
    • Spain: €27,000

Related Topics for further study


Learning Outcomes

  • Ability to collect and analyze data using statistical methods
  • Ability to apply statistical thinking to real-world scenarios
  • Ability to make informed decisions based on data analysis

Prerequisites or good to have knowledge before taking this course

  • Basic knowledge of statistics
  • Familiarity with Microsoft Excel

Course Difficulty Level

Intermediate

Course Format

  • Self-Paced
  • Online
  • Video Lectures
  • Quizzes

Similar Courses

  • Data Analysis and Statistical Inference
  • Applied Data Science with Python
  • Data Science Methodology

Related Education Paths


Notable People in This Field

  • Nate Silver
  • Hans Rosling

Related Books

Description

Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS. By completing this course, students will understand the importance of statistical thinking, and will be able to use data and basic statistical methods to solve many real-world problems. Students completing this course will be able to:

Knowledge

  • How to describe data with statistical summaries, and how to explore your data using advanced visualizations.
  • Understand statistical intervals, hypothesis tests and how to calculate sample size.
  • How to fit, evaluate and interpret linear and logistic regression models.
  • How to build predictive models and conduct a statistically designed experiment.

Outline

  • Course Overview
  • 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

  • Module 1: Statistical Thinking and Problem Solving
  • 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

  • Module 2A: Exploratory Data Analysis, Part 1
  • 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

  • Module 2B: Exploratory Data Analysis, Part 2
  • 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

  • Module 3: Quality Methods
  • 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

  • Module 4: Decision Making with Data
  • 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

  • Module 5: Correlation and Regression
  • 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

  • Module 6: Design of Experiments (DOE)
  • 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 6.23 - 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

  • Module 7: Predictive Modeling and Text Mining
  • 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 and Case Studies
  • Review Questions
  • Case Studies

Summary of User Reviews

This course on Statistical Thinking and Applied Statistics has received great reviews from learners. Students have praised the course for its comprehensive coverage of statistical concepts and practical applications. Overall, learners have found the course to be engaging, informative and useful for their professional development.

Key Aspect Users Liked About This Course

Comprehensive coverage of statistical concepts and practical applications

Pros from User Reviews

  • Engaging and informative course content
  • Practical applications of statistical concepts
  • Course materials are well-organized and easy to follow
  • Flexible learning options to accommodate different schedules
  • Instructors are knowledgeable and responsive to student questions

Cons from User Reviews

  • Some learners found the course to be challenging and required additional effort to keep up
  • The course may not be suitable for learners who have no prior knowledge of statistics
  • Some learners experienced technical difficulties with the online platform
  • The course may not provide enough depth for advanced learners
  • The course may be too basic for learners with significant experience in statistics

Keywords

English
Available now
Approx. 44 hours to complete
Mia Stephens, Ledi Trutna
SAS
Coursera

Instructor

Mia Stephens

  • 4.8 Raiting

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Statistical Thinking for Industrial Problem Solving, presented by JMP

  • 4.8

Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS....

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