COVID-19 Case Infection Forecast (10 pts) -Use the COVID-19 case infection data (provided on sheet P8-Data in the excel template) to conduct a forecast analysis to determine the best exponential smoothing model to forecast COVID-19 Daily Case Infections. Analyze the 3 different exponential smoothing techniques (Simple, Holtz and Winters methods) on the data for forecast models. Use all of the daily case infection data (column c) from July 6^th through August 23^rd, for the analysis. Use the Historical Data (data from June 29^th thru July 5^th) to estimate the Smooth Trend and Seasonal Factors that are to be utilized in the Holtz and Winters Methods. For the Smooth Level initial value, (L₁ for the simplex method, and L₀ for the Holtz and Winters method), use the historical data of daily case infections from July 5^th value which is 46,214 case infections.Estimate a Smooth Trend (To) value by taking the average trend from the historical data. Estimate the seasonal factors Si, using the historical data over the 7 day period. Formulate models for the 3 methods (exponential smoothing, holts and winters) and answer the following questions?

1. What are the optimum alpha, beta and gamma values for the three different models?
2. What is the minimum MAD values for each of the models?
3. What is the August 24^th forecast for daily case infections that day, from the three different models?
4. Which of the 3 model provides the best estimation of forecast for daily case infections? Why?
5. Show a graph of Actual versus forecast for each model

PLEASE, SHOW STEP BY STEP AND EXPLANATIONS

THANK YOU!