In python create a vector x containing 100 observations drawn from a normal distribution N(0,1). Then use the same function to create a vector of noise, e, containing 100 observations drawn from a N(0,0.25) distribution. Use X and e to create a vector y according to the model: Y = -1 + 2X + e. Create a scatterplot displaying the relationship between X and Y and fit a least squares linear model to predict Y using X.