Data Analysis

Distribution

This is a general term for a collection of data. This can be represented visually with graphs or through numerical summaries. “The distribution of test scores shows…”

Categorical Data

Data that is not numerical…this type of data would fall into categories such as hair color, birth month, GPA range…you CANNOT find or make sense of an average value for categorical data.

Quantitative Data

Data that is numerical…this type of data takes on a number value. It does make sense to calculate the average of these values.

Individuals

The set of cases, units, subjects that we collect data on (does not have to be people)

Variables

The value that we collect from the individuals. The variable can be either quantitative or categorical.

Resistant

A statistical measure (mean, median, standard deviation, range, etc) is considered resistant if it isn’t sensitive to extreme values.

Center

We use “center” as a way to describe the middle of the data. This is best done by the mean or the median depending on the shape of the distribution.

Variance

We use “variance” to describe the spread of the data. This is best done with the standard deviation or the inner quartile range depending on the shape of the distribution.

Association

There is an association between two variables if knowing the value of one helps us predict the value of another

Frequency

This is the count of a given variable.

Relative Frequency

This is the percent of individuals that fall into a specific category for a given variable