Mrs. Kong's Math Class

Unit 6 Vocabulary

Line or dot plot: A method of visually displaying a distribution of data values where each data value is shown as a dot or mark above a number line. Also known as a dot plot.

Mean: A measure of center in a set of numerical data, computed by adding the values in a list and then dividing by
the number of values in the list. For example: For the data set {1, 3, 6, 7, 10, 12, 14, 15, 22, 120}, the mean is 21.

Median:  A measure of center in a set of numerical data. The median of a list of values is the value appearing at the
center of a sorted version of the list—or the mean of the two central values, if the list contains an even number of
values. Example: For the data set {2, 3, 6, 7, 10, 12, 14, 15, 22, 90}, the median is 11.

Mode: The number that occurs most frequently in a set of numbers. Example:  We are given these scores of two classes on a mathematics test:
Class 1: 49, 54, 55, 65, 76, 76, 76, 78, 78, 81, 83, 85, 88, 89, 89, 90, 90, 91, 93, 99
Class 2: 55, 59, 60, 65, 68, 76, 77, 77, 78, 80, 80, 80, 89, 89, 90, 92, 92, 95, 96, 98
For Class 1, 76 is the mode (it occurs 3 times). For Class 2, 80 is the mode (it occurs 3 times). Based only on the mode, it seems that Class 2 performed better than Class 1 on the mathematics test.
Do you think the mode is affected by extreme scores like the mean is?

Range: The difference between the largest and smallest values in a numerical data set.  Example:  We are given the following class scores on a mathematics test, arranged in order from lowest to highest scores:
48, 49, 54, 55, 65, 76, 77, 78, 78, 81, 83, 85, 88, 89, 89, 89, 90, 91, 93, 96, 99
The lowest score is 48 and the highest score is 99. Therefore, the range of the test scores = 99 - 48 = 51 points.
variance - A measure of how spread out a set of data is.  The variance provides information about how spread out a set of data is. It is calculated in three steps. First, find the difference between each member of the data set and the mean and square the difference. Second, add up all of the squares of the differences. Third, divide that sum by the number of members minus 1 in the data set. This gives the unbiased estimate for the variance. However if we divide the sum by the number of members in the data set, then we find a biased estimate for the variance.