Explain Bivariate Analysis in Machine Learning?

Bi means two and variate means variable, so here there are two variables. The analysis is related to cause and the relationship between the two variables. There are three types of bivariate analysis.

Bivariate Analysis of two Numerical Variables (Numerical-Numerical)

• Ø Scatter Plot: A scatter plot represents individual pieces of data using dots. These plots make it easier to see if two variables are related to each other. The resulting pattern indicates the type (linear or non-linear) and strength of the relationship between two variables.

• Ø Linear Correlation: Linear Correlation represents the strength of a linear relationship between two numerical variables. If there is no correlation between the two variables, there is no tendency to change along with the values of the second quantity.

Bivariate Analysis of two categorical Variables (Categorical-Categorical)

• Ø Chi-square Test: The chi-square test is used for determining the association between categorical variables. It is calculated based on the difference between expected frequencies and the observed frequencies in one or more categories of the frequency table.

Bivariate Analysis of one numerical and one categorical variable (Numerical-Categorical)

• Ø Z-test and t-test: Z and T-tests are important to calculate if the difference between a sample and population is substantial.