Analyzing Mean and Median with Technology

Sep 16, 2024

Lecture Notes: Mean and Median Analysis with Technology

Introduction

  • Focus on analyzing mean and median using technology.
  • Consideration of different groups and their characteristics.

Poor Sleep Quality

  • Using technology to assess poor sleep quality scores.
  • Data distribution analysis using box plot and histogram.
  • Interpretation of data:
    • LARCs: Mean = 571, Median = 5
    • Owls: Mean = 751, Median = 8
    • Neither: Mean = 602, Median = 6
  • Observations:
    • Higher scores indicate poor sleep quality.
    • Owls have the highest mean and median scores for poor sleep quality.

Alcoholic Drinks Analysis

  • Transition to analyzing alcoholic drinks data.
  • Similar steps using technology to evaluate data.
  • Key statistics:
    • LARCs: Mean = 6.39, Median = 3
    • Owls: Mean = 7, Median = 7
    • Neither: Mean = 5.57, Median = 5
  • Observations:
    • Owls have higher scores in both mean and median.
    • Consistent pattern observed across poor sleep and drinking habits.

Data Interpretation and Patterns

  • Discussion on the relationship between sleep quality and drinking habits.
  • No direct causation can be inferred between poor sleep and high drinking rates.
  • Important to note the distinction between correlation and causation.

Skewness in Data

  • Explanation of skewed data:
    • Right Skew: Tail on the right side, inflates mean.
    • Left Skew: Tail on the left side, deflates mean.
  • Impact of outliers on mean and median:
    • Median remains a reliable measure of center despite skewness.
    • Examples of skewness in real-life scenarios (e.g., housing prices, grades).

Summary

  • Review of concepts covered:
    • Mean: Sum of all values divided by total number of values.
    • Median: Middle value in a data set.
  • Both mean and median as measures of center.
  • Importance of using technology to find mean and median.
  • Practical application: Night people tend to have poor sleep quality and higher drinking rates, but causation is not established.

Conclusion

  • Encouragement to practice the concepts learned through exercises.
  • Reminder of the correlation vs. causation concept.
  • End of session and preparation for the next video.