Chapter 1 Flashcards
individual
objects described by a set of data`
variable
any characteristic
categorial variable
puts an individual into one of several groups
quantitative variable
takes numerical values to find an average
some exceptions ie zip code
distribution
shows what values a variable takes and how often it occurs
frequency table
displays counts (frequencies) of individuals in a category
relative frequency table
shows percentages (relative frequencies) of individuals in a category
roundoff error
rounded percents only come close to 100%
only points out the effects of rounding off results
marginal distribution
a distribution of values for one variable
conditional distribution
separate distributions for each variable
association
exists between two variables if knowing the value of one helps predict the other
symmetric distribution
left and right are mirror images
skewed left/right
a tail to the left/right side
unimodal
single peak
bimodal
multimodal
two peaks
three or more peaks
stemplots
gives a quick peak into the shape of a distribution
histogram
only for QUANTITATIVE data
% on the y axis, values on the x axis
mean
average, how large data would be if data was split evenly
x bar – sample
mew – population
this is for symmetric data `
median
midpoint of distribution
if the number of observations is odd, the median is the center observation
if the number of observations is even, the median is the average of the two center observations
this is for skewed data
range
largest data value - the smallest view
should be ONE value
mode
value that occurs most often
interquartile range
Q1 – 25%; to the left of the median
Q2 – 50%; the median
Q3 – 75%; to the right of the median
1.5 IQR Rule
allows for an observation to be called an outlier if it is outside Q1 - 1.5IQR and Q3 - 1.5IQR
boundaries
five number summary
minimum, Q1, median, Q3, maximum
