Strong Bad Email Statistics

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No Loafing!

Various statistics of interest involving Strong Bad Email data.

Strong Bad Email By Length

This section involves data taken from the list Strong Bad Email By Length.

A scatter plot of chronological number vs. length, with outliers.

• The scatter plot shows a fairly strong positive correlation between Email Number and Email Length. The r value between these two variables without deleting outliers is .844.
  • A r value of 1 would indicate a perfect, positive correlation. A value of -1 indicates a perfect, negative correlation. Therefore, .844 indicates a fairly strong, positive correlation.
• This plot shows there are a handful of clear outliers which are likely effecting the correlation. In the plot below, the outliers have been removed. A Least Squares Regression Line (LSRL) has also been added.
  • The outliers were defined as those emails with a residual value of 40 or greater, or -40 or less.

A scatter plot of chronological number vs. length, without outliers.

• The LSRL can be used to extrapolate, or guess the length of future emails. The r value of this line is .946.
  • The equation for the LSRL is y = 1.3848x + 44.831. y = Time (seconds); x = Email number
• This method of guessing is not 100% accurate, since it is unlikely the e-mails will ever be, say, 20 minutes long. This equation should not be considered a foolproof method for guessing the length of an e-mail.