MISSION 309

Z-scores

In the previous lessons in this Statistics Intermediate course, we focused on distributions as a whole and learned to summarize entire distributions and measure their variability. In this lesson, we'll switch the focus to the individual values in a distribution and learn a few statistical techniques that can help us answer practical questions.

In this lesson, you will locate and compare values on a distribution using z-scores. Z-scores represent the number of standard deviations away from the mean and allow for a more robust analysis.

While exploring how different measures of variability to represent a distribution, we’ll work as a data analyst for a real estate company. We want to find the best neighborhood in Ames, Iowa to invest in (remember: our dataset describes sale prices for houses in Ames). Our company wants to buy a couple of houses that we can then rent and ideally sell back later at a higher price. We think that location is an important factor driving rental and sale prices, and we want to target our investment based on location.

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 what z-scores are.
  • Learn how to standardize a distribution.
  • Learn how make comparisons using z-scores.

Mission Outline

1. Individual Values
2. Number of Standard Deviations
3. Z-scores
4. Locating Values in Different Distributions
5. Transforming Distributions
6. The Standard Distribution
7. Standardizing Samples
8. Using Standardization for Comparisons
9. Converting Back from Z-scores
10. Next steps
11. Takeaways

statistics-intermediate

Course Info:

Intermediate

The median completion time for this course is 7.4 hours. View Details

This course requires a basic subscription. It includes five missions, and one guided project. It is the 16th course in the Data Analyst in Python and Data Scientist in Python path.

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