In univariate analysis, variables are explored one by one. Method to perform univariate analysis will depend on whether the variable type is categorical or continuous. In the case of continuous variables, we need to understand the central tendency and spread of the variable. For example- central tendency – mean, median, mode, max, min, etc.; a measure of dispersion – range, quartile, IQR, variance, standard deviation, skewness, kurtosis etc; visualization methods – histogram, boxplot etc. Univariate analysis is also used to highlight missing and outlier values. The relationship between two variables can be determined using bivariate analysis. How the two variables are associated and/or dis-associated are looked into considering the significance level of comparison. Typically bivariate analysis can be performed for: two categorical variables categorical and continuous variables two continuous variables Different approaches/methods need to be used to handle the above scenarios. Scatter plot can be used irrespective of whether a relationship is linear or nonlinear. In order to figure out how loosely or tightly both variables are correlated, correlation can be performed where the correlation values indicate from -1 to 1. If the value indicates 0, then there is no correlation between the two variables. If it is -1, then there is a perfect -ve correlation and if it is a +1 then it is a perfect +ve correlation.