TPS 5e Chapter 1

Section 1.1 Analyzing Categorical Data

Data Analysis

  • Roller coaster data includes:

    • Type (categorical)

    • Height (Quantitative)

    • Design Speed (Quantitative)

    • Duration (Quantitative)

Key Concepts

  • Categorical Data:

    • Labels for categories; example: gender, college type.

  • Quantitative Data:

    • Numerical values; example: height, speed, duration.

Marginal Distribution

  • Distribution of one variable calculated from a two-way table.

Conditional Distribution

  • Distribution of a variable based on specific values of another variable.

Example Analysis : Colleges & Rankings

  • Categorical Variables: Type of college, rankings from 1-5 (Satisfactory).

  • Quantitative Variables: Average GPA, Acceptance Rates.

Students and TV Viewing Habits

  • Categorical Variables: Gender, preferred TV genre.

  • Quantitative Variables: Hours spent watching TV, number of shows watched.

Page 9: Radio Station Formats

Frequency Tables

  • Count of stations by format; includes:

    • Adult Contemporary: 1556 (11.2%)

    • Country: 2066 (14.9%)

  • Importance of consistency in data (total should equal overall population).

Page 10: Bar Graphs and Pie Charts

  • Visualization to display categorical data.

    • Bar graphs show counts/percent.

    • Pie charts show part-to-whole relationship.

Page 11: MP3 Player Ownership by Age Group

Data Interpretation

  • Younger people tend to own MP3 players.

  • Task: Make a well-labeled bar graph, analyze age group's ownership.

  • Pie charts inappropriate due to differing percent meanings.

Page 12: Pictographs

  • Caution found in pictographs, misleading representations; emphasis on area vs. height.

Two-Way Tables and Marginal Distributions

  • Analysis of categorical data with two variables.

Page 13: Conditional Distributions in Action

Example: Survey Results

  • Young adult's opinions on wealth by gender.

  • Distribution analysis separating by gender.

Page 14: Analyzing Data and Graphs

  • Analyzing varying perception of wealth based on existing factors.

Page 15-18: Relationships and Associations

Investigating Associations

  • Gender opinion about financial future relationship analysis.

  • Consider both conditional and marginal distribution to reveal relationships between variables.

Summary

  • Review results of survey; ask whether data supports the expected outcomes.

  • Mean and median definitions explored; statistical importance in data representation emphasized throughout.