It is a big obstacle to deal with missing values during data analysis. Missing values can occur for a number of reasons like skipped response, incomplete data entry, lost files, data malfunctions, etc. These missing values have consequences like biased analysis, model instability, incorrect inference, and loss of information, and they mostly reduce the statistical

In part 1 of this tutorial, we examined how a categorical variable can affect our regression model. However, most of the time, our model not only depends on more than one categorical variable, but also on a continuous variable; education, gender, and age can together play a significant role in predicting salary levels. This tutorial

ANCOVA is a statistical term that is used to compare the means of a dependent variable among two or more groups and co-variates. It combines the ANOVA and regression as it contains at least one categorical (factors) and one continuous (covariates) independent variable. Co-variates, known as confounding variables, are continuous independent variables in the model

Descriptive statistics, is like a snapshot of data, that provides the quick look of data before we delve deeper into data handling and manipulation. It helps us understand the features of data like mean, median, mode, and frequency of the data. From these summary statistics, one can visualize data by creating different kinds of interesting