Difference between revisions of "Objectives 2018F"
From Sean_Carver
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== Objectives for Exam 1 == | == Objectives for Exam 1 == | ||
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| + | * 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 outliers 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. | ||
Revision as of 16:24, 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 outliers 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.
