Bivariate analysis is concerned with the relationships between pairs of variables (X, Y) in a data set. Bivariate analysis is the simultaneous analysis of two variables. Bivariate analysis explores the concept of association between two variables. Univariate Analysis is a form of quantitative analysis of data where each variable is analyzed in isolation. Bivariate analysis is based on how two variables simultaneously change together. Bivariate analysis is usually undertaken to see if one variable is related to another variable. Multivariate analysis is the simultaneous analysis of three or more variables.

It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. To begin the bivariate analysis, the first step is to construct a scattergram to illustrate the relationship. Each dot represents a paired value from the sample, and the scattergram reveals a typical oval shape that is due to central tendency.

Bivariate analysis should be useful in supporting or failing to support the arguments of dependency or dependent development theorists that there is an association between dependency and underdevelopment. Using bivariate analysis we test hypotheses of "association" and causality. Association refers to the extent to which it becomes easier to know or predict a value for the dependent variable if we know a case's value on the independent variable.

Bivariate analysis helps compare and control two or more related variables in situations where quality depends on the combine effect of these variables. This method is most useful when two different variables work together to affect the acceptability of a process or part thereof. Bivariate analysis is the simplest form of quantitative analysis. Bivariate analysis involves the analysis of two variables for the purpose of determining the empirical relationship between them.

**Scatter plots**

These give you a visual
idea of the pattern that your variables follow. In terms of correlation,
graphical plots are called scatterplots. Scatterplots can show you visually the
strength of the relationship between the variables, the direction of the
relationship between the variables, and whether outliers exist.

**Regression Analysis****
**
Regression
analysis refers to a variety of tools that you can use to determine how your
data points might be related. Bivariate Regression Analysis is used during the
analysis and reporting stage of quantitative market research.

The correlation coefficient is a measure of the degree of relationship present between the linearly related variables. Correlation is a bivariate analysis that measures the strength of association between two variables and the direction of the relationship.

The value of the correlation coefficient varies between +1 and -1. The correlation coefficient tells you if the variables are related. A zero means they aren’t correlated, while a 1 means that the variables are perfectly correlated.