Ch.2.1: Frequency Distributions & Graphs

What are some benefits of representing data sets using frequency​ distributions?

Organizing the data into a frequency distribution can make patterns within the data more evident.

Why should the number of classes in a frequency distribution be between 5 and​ 20?

If the number of classes in a frequency distribution is not between 5 and​ 20, it may be difficult to detect any patterns.

relative frequency histogram

frequency histogram that measures *relative* frequencies in the y scale, rather than frequencies (x scale = classes, y scale = relative frequencies in % or 0.0)

range

the difference between the maximum and minimum data entries

class width

the distance between lower OR upper limits of consecutive classes

upper class limit

the greatest number that can belong to the class

lower class limit

the least number that can belong to the class

class boundaries

the numbers that separate classes *without* forming gaps between them

relative frequency

the portion OR percentage of the data that falls in a class (class frequency divided by sample size)

cumulative frequency

the sum of the frequencies of a class and all previous classes

midpoint

the sum of the lower and upper limits of a class, divided by two (the average of the class)

What are some benefits of using graphs of frequency​ distributions?

It can be easier to identify patterns of a data set by looking at a graph of the frequency distribution.

frequency histogram

a bar graph that represents the frequency distribution of a data set (x scale = classes, y scale = frequencies of class)

cumulative frequency graph (*ogive*)

a line graph that displays the cumulative frequency of each class at its upper class boundary (x scale = upper boundaries, y scale = cumulative frequencies)

frequency polygon

a line graph that emphasizes the continuous change in frequences

frequency distribution

a table that shows *classes* (intervals) of data entries with a count of the number of entries in each class