1. Using your calculator, find an exponential function that fits the data for the COVID-19 confirmed cases in the US. Consider March 21, 2020 as day zero. Use the exponential regression of the form y=Ab^x and keep all decimals for the constants A and b.
2. Using the change of base formula Ab^x = Ae^(lnbx) write your exponential function in base e, so that your function looks like f(x) = Ae^(rx), where A and r are constants. Graph your function.
3. Find the linearization of the function you found in question 2 at day x=12 (April 1st). Call this linearization L(x). Graph L(x), together with the original function f(x) 4. "Predict" the number of cases on April 4th and 5h in two ways. The first way by using the exponential fit f(x) = Ae^(rx) that you found in question 2. The second way is by using the linearization. Now compare the prediction with the number of confirmed cases on April 4th and 5th. Which prediction is better and why? Is the number of confirmed cases the same as the number of actual cases?
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