Unit 1 - Topics 6 and 7 Flashcards
Distribution
The distribution of a quantitative variable slices up all the possible values of the variable into equal-width bins and gives the number of values (or counts) falling into each bin.
Histogram (relative frequency histogram)
A histogram uses adjacent bars to show the distribution of a quantitative variable. Each bar represents the frequency (or relative frequency) of values falling in each bin.
Gap
A region of the distribution where there are no values.
Stem-and-Leaf Display
A stem-and-leaf display shows quantitative data values in a way that sketches the distribution of the data. It’s best described in detail by example.
Dot plot
A dot plot graphs a dot for each case against a single axis.
Shape
To describe the shape of a distribution, look for the following:
-single vs. multiple modes
-symmetry vs. skewness
-outliers and gaps
Center
The place in a distribution of a variable that you’d point to if you wanted to attempt the impossible by summarizing the entire distribution with a single number. Measures of the center include the mean and median.
Spread
A numerical summary of how tightly the values are clustered around the center. Measures of spread include the IQR and standard deviation.
Mode
A hump or local high point in the shape of the distribution of a variable. The apparent location of modes can change the scale of a histogram is changed.
Unimodal (Bimodal)
Having one mode. This is a useful term when describing the shape of a histogram when it’s generally mound-shaped. Distributions with two modes are called bimodal. Those with more than two are multimodal.
Uniform
A distribution that’s roughly flat is said to be uniform.
Symmetric
A distribution is symmetric if the two halves on either side of the center look approximately like mirror images of each other.
Tails
The tails of a distribution are the parts that typically trail off on either side. Distributions can be characterized as having long tails (if they straggle off for some distance) or short tails (if they don’t).
Skewed
A distribution is skewed if it’s not symmetric and one tail stretches out farther than the other. Distributions are said to be skewed left when the longer tail stretches to the left and skewed right when it goes to the right.
Outliers
Outliers are extreme values that don’t appear to belong with the rest of the data. They may be unusual values that deserve further investigation, or they may just be mistakes; there’s no obvious way to tell. Don’t delete outliers automatically-you have to think about them. Outliers can affect many statistical analyses, so you should always be alert to them.
