This code is for performing Exploratory Data Analysis (EDA) and linear regression on the "mtcars" dataset. EDA is a process of understanding the data by visualizing the data, summarizing the data, and finding patterns in the data. The code starts by importing necessary libraries and reading in the "mtcars" dataset. Then, it displays the first few rows of the dataset and some statistical summary of the data. Next, it creates some scatterplots and countplots to visualize the relationships between different variables in the dataset. It also creates a boxplot to visualize the distribution of the "price" variable.

The code then performs a one-variable linear regression to predict the "highwaympg" variable based on the "price" variable. Linear regression is a statistical method used to model the linear relationship between a dependent variable and one or more independent variables. The code fits a linear regression model using the "price" variable as the independent variable and the "highwaympg" variable as the dependent variable. It then prints the coefficient of the "price" variable and creates a scatterplot with a fitted regression line.

Finally, the code performs a multiple linear regression to predict the "price" variable based on the "horsepower" and "curbweight" variables. It fits a linear regression model using the "horsepower" and "curbweight" variables as the independent variables and the "price" variable as the dependent variable. It then prints the coefficients of the "horsepower" and "curbweight" variables.