The simplest form of statistical analysis incorporates tools to figure out the middle range of data measured. Practices such as measuring the mean, the mode, and the average of a data set represent these basic tools. They are typically taught first in statistical analysis courses to teach students how to begin manipulating data for results.
The process of measuring behavior of data when two variables may be unknown and the data moves in a sequential pattern is known as linear regression. By breaking down at each point where the other variable results in allows a user to graph a line on a graph to measure the data with different inputs of data. The model commonly uses a process of solving for X and Y in a mathematical formula.
Where data involves multiple variables that can change simultaneously, multiple regression is used to determine measurements. This statistical model allows a user to place multiple variables into a mathematical model and solve for them at different data points. The result graphically can begin to look like a three dimensional shape as it measures data with three or more different metrics. This sort of statistical analysis tends to be frequently used in stock portfolio analysis.
In some cases, the statistical analysis is focused more on the change that occurs within an entire data population rather than specific points. Statistical formulas for measuring variation or standard deviation get used for this purpose. Practical applications involve determining change to confirm tolerances or ranges of acceptable change before measurements exceed acceptable limits.