Statistical Thinking Background

Statistical Thinking for Industrial Problem Solving

A free online statistics course

Exploratory Data Analysis

Exploratory data analysis (EDA) is an investigative process in which you use summary statistics and graphical tools to get to know your data and understand what you can learn from it.

With EDA, you can uncover patterns in your data, understand potential relationships between variables, and find anomalies, such as outliers or unusual observations. The goal is to generate interesting questions or hypotheses that you can test using more formal statistical methods.

exploratory-data-analysis
Gray gradation

Specific topics covered in this module include:

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

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