There are 2 things that should be done before doing regression analysis.they are?

1. Data cleaning and preparation: Before conducting regression analysis, it is important to clean and prepare the data. This includes checking for missing values, outliers, and errors in the data, as well as transforming variables if necessary.

2. Assumptions checking: It is also important to check the assumptions of regression analysis, such as linearity, independence, homoscedasticity, and normality of residuals. If these assumptions are violated, it can affect the validity of the regression results.

1. Exploratory data analysis (EDA): This involves exploring the data to identify patterns, outliers, and potential relationships between variables. EDA can help you identify potential problems with the data, such as missing values or collinearity, and can also provide insights into the relationships between the variables that you are interested in.
2. Model selection: This involves choosing the type of regression model that you will use. There are many different types of regression models, each with its own advantages and disadvantages. The best model for your data will depend on the specific goals of your analysis and the characteristics of the data.