Univariate linear regression *Note: Solutions to this problem must follow the method described in class and the linear regression handout. There is some flexibility in how your solution is coded, but you may not use special functions that automatically perform linear regression for you.* Load in the BrainBodyWeight. csv dataset. Perform linear regression using two different models: M1: brain_weight = WO + w1 x body_weight M2: brain_weight = WO + w1 x body_weight + w2 x body_weight^2
a. For each model, follow the steps shown in class to solve for w. Report the model, including w values and variable names for both models.
b. Use subplots to display two graphs, one for each model. In each graph, include: • Labeled x and y axes Title • Scatterplot of the dataset • A smooth line representing the model
c. For each model, calculate the sum squared error (SSE). Show your 2 SSE values together in a bar plot.
d. Which model do you think is better? Why? Is there a different model that you think would better represent the data?