STATISTICS

Course Credits: 3 Units

Prerequisites: Stat 123

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STAT 133 - Introduction to Exploratory Data Analysis

Course Description

Exploratory data analysis techniques; robust estimators for location and scale parameters.

Course Learning Outcomes

At the end of this course, students will:

  1. Recognize the need and importance of exploratory techniques in studying and summarizing the major characteristics of any data set;
  2. Apply the different exploratory techniques in studying and summarizing data sets;
  3. Utilize robust estimators that are useful in cases where the assumptions of an underlying probabilistic model are not satisfied; and
  4. Interpret the results derived from data sets after completing the analysis.
Course Outline

UNIT 1. Introduction

  1. What is EDA?
  2. Broad Phases of Data Analysis
  3. Main Themes of EDA
  4. Objectives of Graphical Methods

UNIT 2. Stem-and-Leaf Display

  1. The Basic Display
  2. Some Variations

UNIT 3. Letter Values

  1. Sorting and Ranking
  2. Letter Values and Letter-Value Displays
  3. Spreads
  4. Outside Cut-offs

UNIT 4. Boxplots and Batch Comparison

  1. The Boxplot for a Single Batch
  2. Comparing Batches Using Boxplots
  3. Quantile Plots and Empirical Quantile-Quantile Plots
  4. The Spread-versus-Level Plot

UNIT 5. Transforming Data

  1. Reasons for Transforming
  2. Power Transformations
  3. Transforming for Symmetry
  4. Transforming for Other Data Structures
  5. Matched Transformations

UNIT 6. Resistant Lines for y versus x

  1. Slope and Intercept
  2. Summary Points
  3. Finding the Slope and the Intercept
  4. Residuals
  5. Polishing the Fit
  6. Outliers
  7. Straightening Plots by Re-expression

UNIT 7. Analysis of Two-Way Tables

  1. Two-Way Tables
  2. Median Polish
  3. Non-Additivity and the Diagnostic Plot

UNIT 8. Smoothing Data

  1. Data Sequences and Smooth Summaries
  2. Elementary Smoothers
  3. Compound Smoothers

UNIT 9. Examining Residuals

  1. Residuals and the Fit
  2. Residuals as Batches
  3. Residual Plots
  4. Rootograms

UNIT 10. Location Estimators

  1. Main Concepts
  2. Simple L-Estimators
  3. M-Estimators
  4. Distributions
  5. Choosing Robust Estimators

UNIT 11. Geographical Information System