MISSION 398

Comparing Frequency Distributions

In the Visualizing Frequency Distributions lesson, we learned what graphs we can use to visualize the frequency distribution of any kind of variable. In this mission, we'll learn how to compare frequency distributions with visualization. In addition, we will learn about the types of graphs we can use to compare multiple frequency distributions at once.

You will build on the concepts you learned in Visualizing Frequency Distributions, learning additional visualizations such as step-type histograms, kernel density plots, strip plots, and box plots.

In this lesson, we will continue to work with the WNBA dataset while learning how to compare frequency distributions to determine the most played positions on the court for rookies, as well as how rookies compare to veterans with respect to positions on the court.

As you work through each concept, you’ll apply what you’ve learned from within your browser; there's no need to use your own machine to do the exercises. The Python environment inside of this course includes answer-checking to ensure you've fully mastered each concept before moving on to the next.

Objectives

  • Learn to compare frequency distributions.
  • Learn about grouped bar plots.
  • Learn about overlaid histograms
  • Learn about kernel density estimate plots.
  • Learn about scatter plots and box plots.

Mission Outline

1. Comparing Frequency Distributions
2. Grouped Bar Plots
3. Challenge: Do Older Players Play Less?
4. Comparing Histograms
5. Visualizing the Mean with Histograms
6. Frequency Polygons and Kernel Density Estimate Plots
7. Drawbacks of Kernel Density Plots
8. Scatter Plots
9. Box plots
10. Outliers
11. Next steps
12. Takeaways

statistics-fundamentals

Course Info:

Beginner

The median completion time for this course is 6.75 hours. View Details

This course requires a basic subscription. This course includes six missions, and one guided project. It is the eighth course in the Data Analyst in R path.

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