$(1)$ Create the forecast for period $53$ using an exponential smoothing with a smoothing constant of $0\mathrm{.}6.$ Make your starting forecast for Week $2$ a Naive forecast from Week $1.$ Round up answer to nearest whole number

$(2)$ Create the forecast for period $53$ using a linear regression. Round up answer to nearest whole number:

$(3)$ What is the historical Mean Absolute Deviation $($MAD$)$ of each of the exponential smoothing forecast with a a smoothing constant of $0\mathrm{.}6?$ Round up to the nearest whole number.

$(4)$ What is the historical Mean Absolute Deviation $($MAD$)$ of a linear regression forecast? Round up answer to nearest whole number

$(5)$ What is the historical average weekly bias of exponential smoothing forecast with a smoothing constant of $0\mathrm{.}6?$ Round up to the nearest whole number.

$(6)$ What is the historical average weekly blas of the linear regression forecast?

Round up to the nearest whole number

$(7)$ Suppose you go to this thing called Google and figure out how to calculate an exponential smoothing with trend adjustment forecast. You run the numbers and the MAD is $65$ and the average bias is $-17\mathrm{.}3.$ What logic below would be reasonable for implementing the exponential smoothing with trend adjustment forecast or the regression forecast that we already calculated?

All of the above are reasonable approaches to forecasting demand for this product Use multiple forecasts weighted by accuracy Implement exponential smoothing with trend adjustment because it is more accurate and you can adjust for bias

Implement the regression forecast because the accuracy is close to the exponential smoothing with trend adjustment and shows signs of no bias