Statistical Methods (Category:Concepts, Level:Basic )

The appropriate statistical method for your data also depends on the number of variables involved. Univariate analysis provides techniques for analyzing and describing a single variable at a time. Univariate analysis reveals patterns in the data, by looking at the range of values, measures of dispersion, the central tendency of the values, and frequency distribution. It also summarizes large amounts of data and organizes data into graphs and tables so that it is more easily understood.

Bivariate analysis describes and explains the relationship between two variables and how they change, or covary, together. It includes techniques such as correlation analysis and chi-square tests of independence.

Multivariate or multivariable analysis examines two or more variables at the same time, in order to understand the relationships among them. Techniques such as multiple linear regression and n-way ANOVA are typically called multivariable analyses because there is only one response variable. Techniques such as factor analysis and clustering are typically called multivariate analysis because they consider more than one response variable. Multivariate linear regression and multivariate ANOVA (MANOVA) are extensions of these techniques when there is more than one response variable. You learn many of these statistical methods in this course.