Question Help The accompanying data is monthly data for a large retailer's revenue, along with several possible related economic variables. Complete parts a) and b) below. PE: Click the icon to view the revenue data. a) Using technology, find the regression equation predicting revenues from the Retail Sales index, the Consumer Price index (CPI), and Personal Consumption (PC). Revenue = + ( CPI+ PC+ Retail Sales index (Round the intercept to one decimal place as needed. Round the CPI coefficient to three decimal places as needed. Round the remaining coefficients to six decimal places as needed.) b) Does it seem that the retailer's revenue is closely related to the general state of the economy? The value of R2 for this model is %. At a=0.05, the t-ratio for CPI significant, the t-ratio for PC significant, and the t-ratio for Retail Sales index V significant. It looks like these variables account for much of the variation in the retailer's revenue. (Round to one decimal place as needed.) Question Help The accompanying data is monthly data for a large retailer's revenue, along with several possible related economic variables. Complete parts a) and b) below. PE: Click the icon to view the revenue data. a) Using technology, find the regression equation predicting revenues from the Retail Sales index, the Consumer Price index (CPI), and Personal Consumption (PC). Revenue = + ( CPI+ PC+ Retail Sales index (Round the intercept to one decimal place as needed. Round the CPI coefficient to three decimal places as needed. Round the remaining coefficients to six decimal places as needed.) b) Does it seem that the retailer's revenue is closely related to the general state of the economy? The value of R2 for this model is %. At a=0.05, the t-ratio for CPI significant, the t-ratio for PC significant, and the t-ratio for Retail Sales index V significant. It looks like these variables account for much of the variation in the retailer's revenue. (Round to one decimal place as needed.)