Measures of Central Tendencies and Basic Variation

What measure of central tendency (mean, median or mode) would be best for the following set of data?

0, 89, 92, 97, 99, 100

(Note: An outlier is a number that is an abnormal distance away from the others)

• Mode, because it is the one that shows up the most.

• Median, because there is an outlier. The outlier could be incorrect and would influence the mean too much.

• Mean, because there is not an outlier. You should always use the mean.

• There is not a best measure of central tendency for this data.

Ellana scores these number on her spelling test: 4, 6, 7.  She would like her mean to be 6.  What will she have to score next?

• 17
• 24
• 7
• 2

• mean

• median

• mode

• range

• mean

• median

• mode

• range

• mean

• median

• mode

• range

• mean

• median

• mode

• range

Which is NOT true about the Interquartile Range?

• It measures the range of the middle half of the data.

• It is the range between the one-quarter and three-quarter mark.

• It is the average of the one-quarter and three-quarter mark.

• It gives a more accurate range since it excludes outliers.

There are 50 numbers in our data set. If the range of our data is 100 and the Interquartile Range is 5, this tells us that:

• the data most likely has one or more outliers.

• all the data is really spread out.

• all the data is really close together.

A business man wants to buy an egg farm. The mean of both farms is 50 eggs per day and range of his two options are as follows:

A: range=8

B: range=75

Which farm would he buy?

• Farm A because a smaller range means that most of the actual number of eggs is only 8 eggs away from 50.

• Farm B because a large mean means that most numbers are spread out, some very small, some very large.

• Either Farm A and B.

Which statement is NOT true about quartiles?

• Quartiles divide the data into sections of 25%.

• Quartiles divide the data into 4 parts.

• There are 4 quartiles.

• the median is a special quartile that divides the dat into two halves.