Difference between revisions of "Objectives 2018F"

From Sean_Carver
Jump to: navigation, search
(Objectives for Exam 1)
Line 8: Line 8:
 
* 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 many/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 the variables.
 
* 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.
 
* Make a stem plot from data.
 
* Make a stem plot from data.

Revision as of 18:01, 16 September 2018

Objectives for Exam 1

  • In looking at side-by-side box plots, be able to tell which distribution has 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 (both modified and unmodified), be able to tell which distribution has the which has the greatest and least values (i.e. min/max).
  • 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 the variables.
  • 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 the following: a bar graph, pie chart, stem plot, histogram, box plot.
  • 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, lower tail, or both), center (mean and median), spread (standard deviation, and 5 number summary).
  • Know that a distribution is skewed to the side of the longer tail, but alternatively, and perhaps more precisely, a distribution is skewed left if its mean is less than its median, and skewed right if its mean is greater than its median---the longer tail pulls the mean toward it, as the mean is more sensitive to outliers.
  • Know how to compute the mean and median, and the standard deviation and the 5 number summary of a variable from data in StatCrunch.
  • 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.
  • In StatCrunch, know how to derive a frequency table for a categorical variable to describe its distribution.