Statistics for Data Science and Business Analysis - Practical Example: Regression Analysis

The video tutorial covers the process of analyzing car sales data to predict used car prices. It begins with an introduction to the dataset and the importance of data cleaning. The tutorial then identifies key variables for regression analysis, such as brand, mileage, engine volume, and year of production. It discusses the assumptions necessary for regression and demonstrates how to create and evaluate regression models. The video concludes with an interpretation of the regression results, highlighting the significance of each variable and the use of dummy variables for categorical data.

What assumptions must be checked before running a regression analysis?

What is the impact of multicollinearity on regression analysis?

Explain the significance of the intercept in the regression equation.

How does the year of production affect the price of a used car according to the regression model?