Difference between revisions of "Objectives 2018F"
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* Know how to describe in words the distribution of a quantitative variable, in terms of shape (unimodal, bimodal, multimodal, symmetric, skewed to left, skewed to right), outliers (how many and where they are, e.g. upper tail or lower tail), center (mean and median), spread (standard deviation, and 5 number summary). | * Know how to describe in words the distribution of a quantitative variable, in terms of shape (unimodal, bimodal, multimodal, symmetric, skewed to left, skewed to right), outliers (how many and where they are, e.g. upper tail or lower tail), center (mean and median), spread (standard deviation, and 5 number summary). | ||
* If a distribution has two or more isolated modes, know how to use a "where" function to separate the modes so that summary statistics can be derived for each mode in isolation. | * If a distribution has two or more isolated modes, know how to use a "where" function to separate the modes so that summary statistics can be derived for each mode in isolation. | ||
| + | * Know which summary statistics are resistant to outliers (i.e. "resistant") and which are sensitive to outliers (i.e. "not resistant"). Also understand why. | ||
| + | * Know that describing the distribution of a variable conveys "what values the variable takes, and how often it takes those values." Know the similarities and differences between describing distributions of categorical and quantitative variables. | ||
Revision as of 17:14, 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."
- Know what type of variable is described by each of: a bar graph, pie chart, stem plot, histogram, box plot.
- Know how to describe in words the distribution of a categorical variable, using StatCrunch to derive summary the appropriate summaries.
- Know how to describe in words the distribution of a quantitative variable, in terms of shape (unimodal, bimodal, multimodal, symmetric, skewed to left, skewed to right), outliers (how many and where they are, e.g. upper tail or lower tail), center (mean and median), spread (standard deviation, and 5 number summary).
- If a distribution has two or more isolated modes, know how to use a "where" function to separate the modes so that summary statistics can be derived for each mode in isolation.
- Know which summary statistics are resistant to outliers (i.e. "resistant") and which are sensitive to outliers (i.e. "not resistant"). Also understand why.
- Know that describing the distribution of a variable conveys "what values the variable takes, and how often it takes those values." Know the similarities and differences between describing distributions of categorical and quantitative variables.
