Difference between revisions of "Objectives 2018F"
From Sean_Carver
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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 | + | * 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. |
* 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 16:27, 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 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.
