Difference between revisions of "Objectives 2018F"

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* In looking at side-by-side box plots, be able to tell which distribution has the greatest iqr, and which has the least iqr.
 
* In looking at side-by-side box plots, be able to tell which distribution has the greatest iqr, and which has the least iqr.
 
* In looking at side-by-side box plots, be able to tell which distribution has the least and greatest Q1 and Q3.
 
* In looking at side-by-side box plots, be able to tell which distribution has the least and greatest Q1 and Q3.
* In looking at side-by-side box plots, be able to tell which distribution has the which has the greatest and least values in both a modified and unmodified box plot.
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* In looking at side-by-side box plots, be able to tell which distribution has the which has the greatest and least values (min/max) in both a modified and unmodified box plot.
 
* Know the 1.5*IQR Rule for suspected outliers.  Be able to use this rule to:
 
* Know the 1.5*IQR Rule for suspected outliers.  Be able to use this rule to:
 
* Plug in a "where" function into StatCrunch/Summary Stats to count (statistic = n) the number of outliers in a box plot when they are too close to count by hand on the image.
 
* Plug in a "where" function into StatCrunch/Summary Stats to count (statistic = n) the number of outliers in a box plot when they are too close to count by hand on the image.

Revision as of 16:28, 16 September 2018

Objectives for Exam 1

  • In looking at side-by-side box plots, be able to tell which distribution is the greatest median, and which has the least median.
  • In looking at side-by-side box plots, be able to tell which distribution has the greatest iqr, and which has the least iqr.
  • In looking at side-by-side box plots, be able to tell which distribution has the least and greatest Q1 and Q3.
  • In looking at side-by-side box plots, be able to tell which distribution has the which has the greatest and least values (min/max) in both a modified and unmodified box plot.
  • Know the 1.5*IQR Rule for suspected outliers. Be able to use this rule to:
  • Plug in a "where" function into StatCrunch/Summary Stats to count (statistic = n) the number of outliers in a box plot when they are too close to count by hand on the image.