In your pursuit of Supply Chain Management efficiency at your company, you realize variation in inventory components demand leads to larger upstream variations in demand. The dreaded bullwhip effect! You are seeking the \"best fit\" model possible. Do a \"first difference calculation\" column, that would ultimately be used in creating an ARIMA regression model, with the following Time Series data: Year & Qtr Co's Demand for Inv. Components First Difference Values 2020, Q1 1.1 million 2020, Q2 8.2 million 2020, Q3 33.4 million 2020, Q4 89.0 million 2021, Q1 171.0 million Use the data from Previous Problem and calculate a "Lag-one sample autocorrelation" column. Another ARIMA/Box-Jenkins method for manipulating data as you seek the "best fit" model. Hint: This method will need the "mean" calculated. Year & Qtr Co's Demand for Inv. Components Lag-1 Samp. Autocorr. Values 2020, Q1 1.1 million 2020, Q2 8.2 million 2020, Q3 33.4 million 2020, Q4 89.0 million 2021, Q1 171.0 million