MASON PSYC 612 - Exploratory Data Analysis (13 pages)

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Exploratory Data Analysis



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Exploratory Data Analysis

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Pages:
13
School:
George Mason University
Course:
Psyc 612 - Advanced Statistics

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PSYC 612 SPRING 2010 Exploratory Data Analysis EDA Lecture Week 4 6 2010 Contents 1 Preliminary Questions 1 2 Part I Exploratory Data Analysis 30 minutes 2 minute break 2 1 Purpose 2 2 Objectives 2 3 Exploratory Data Analysis EDA 2 4 Why explore your data 2 5 Suitable EDA Procedures 2 5 1 Data Summaries 2 5 2 Tabular Procedures 2 5 3 Pseudo graphical Procedures 2 5 4 Graphical Procedures 1 2 2 2 2 2 2 3 5 10 3 Part II Digging deeper into exploratory procedures 10 minutes 2 minute break 11 3 1 Purpose 11 3 2 Objectives 11 4 Part III Matrix algebra FINALLY 1 12 Preliminary Questions Are there any questions about missing data Is everyone ready for the second module Are there any lingering questions about the modules before we begin this week 2 Part I Exploratory Data Analysis 30 minutes 2 minute break 1 2 1 Purpose Introduce you basic concepts of exploratory data analysis 2 2 1 2 3 4 2 3 Objectives Describe exploratory data analysis Provide a rationale for exploring data Outline different approaches Demonstrate various procedures Exploratory Data Analysis EDA Exploratory data analysis is a process not a single procedure where the analyst you uses several procedures to better understand your data The first and now classic text on EDA was written several decades ago by the eminent statistician John Tukey In his book Tukey described various procedures and why they may be useful Those procedures do not constitute EDA Everything goes with EDA You only need to be curious about every variable value and relationship and use tables figures and summary statistics to satisfy your curiosity There are no rules for EDA 2 4 Why explore your data Exploring your data demands time effort and attention but those demands can easily save you hours of frustration How you might ask will exploratory procedures save you hours By understanding your data you avoid common pitfalls such as distributional problems scaling anomalies and outlier observations EDA helps you find these problems before



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