Car Insurance is something you have to pay for to drive a car in most places. The insurance will pay for damage to your car, yourself, or other people in the event of an accident. You pay a monthly amount, called a "premium," based on your liklihood to need to use the insurance (have an accident). We have a few facts: - Car insurance companies have a profit-based interest in understanding exactly the risk their insurance is under. In other words, if you are more likely to recieve an insurance payment they want you to pay more in premiums. - Car insurance companies also rate roads, areas, etc as part of their calculation for determining premiums. - Car insurance companies are now offering discounts to customers for "proving" they drive safely by installing a GPS tracking device in the customer car that sends speed, braking, and location based information to the insurance company. Please provide (as unethical_example ) one example of how the GPS data could result in an unethical result for the customers. Please also explain how it is unethical. No more than one paragraph is expected. Make sure you see your answer when you run the cell and not False . Feel free to come back to this question at the end. for_example = "this text is a hint for " + 1 "how to make your string multiple lines" + I "so that it is easier for YOU to read." + I "You are not required* to use this style for your answer" unethical_example = type(unethical_example) == str and print(unethical_example) Let's take a look at some car buying data. What do you think informs the amount of money people are willing to spend on a car? Once you look at the sample, move on to Q2.1.1 cars = Table.read_table("cars.csv") \# let's add you to the car buyers! cars = cars.with_row( [my_name, "USA", random.randint (0,1), random.randint (17, 25), float(str(my_id) [:5]), float(str(my_id) [:3]), float(str(my_id)[:6]), 34922]) cars.show(5) Please assign the strongest correlation column name to best_corellation . Remember to compare apples to apples. \# choose the best correlation from the table the prior cell printed best_correlation = grader.check("q2_1_2") Question 2.2 like. cars .... ("Age", "Car Purchase Amount") Question 2.3 def slope(tbl, x_column_name, y_column_name): ""'" Computes the slope of the regression line ""." ... def intercept(tbl, x_column_name, y_column_name): ""'" Computes the intercept of the regression line ""." ... cars_slope = cars_intercept =... grader.check("q2_3") et's make some predictions. Using the slope and intercept we calculated above, let's make some preditions. Please assign your predictions to a variable called predictions . lef fitted_values(tbl, x_column_name, y_column_name): ""'"Return an array of the regressions estimates at all the x values""" .. redictions =