Difference between revisions of "Objectives 2018F"
From Sean_Carver
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* In looking at side-by-side box plots (modified) be able to tell which show that suspected outliers are present. | * In looking at side-by-side box plots (modified) be able to tell which show that suspected outliers are present. | ||
* 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 many/too close to count by hand on the image. |
* Given a distribution, identify the cases and variable. | * Given a distribution, identify the cases and variable. | ||
* Given a list of variables, identify which are nominal, ordinal, binary, identifier/label, or quantitative. Be able to justify your answer, not just provide a guess. | * Given a list of variables, identify which are nominal, ordinal, binary, identifier/label, or quantitative. Be able to justify your answer, not just provide a guess. | ||
Revision as of 16:45, 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.
- In looking at side-by-side box plots (modified) be able to tell which show that suspected outliers are present.
- 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 many/too close to count by hand on the image.
- Given a distribution, identify the cases and variable.
- Given a list of variables, identify which are nominal, ordinal, binary, identifier/label, or quantitative. Be able to justify your answer, not just provide a guess.
- Make a Stem Plot from data.
- Be able to follow the direction: "make a stem plot and split stems."
- Be able to follow the direction: "make a stem plot and trim stems" and/or "make a stem plot and trim and split stems."
