Revenue CPI Personal_Consumption Retail_Sales_Index 14.764 552.7 7868495 301337 23.106 552.1 7885264 357704 12.131 554.9 7977730 281463 13.628 557.9 8005878 282445 16.722 561.5 8070480 319107 13.98 563.2 8086579 315278 14.388 566.4 8196516 328499 18.111 568.2 8161271 321151 13.764 567.5 8235349 328025 14.298 567.6 8246121 326280 17.169 568.7 8313670 313444 13.915 571.9 8371605 319639 15.739 572.2 8410820 324067 26.177 570.1 8462026 386918 13.17 571.2 8469443 293027 15.139 574.5 8520687 294892 18.683 579 8568959 338969 14.829 582.9 8654352 335626 15.697 582.4 8644646 345400 20.234 582.6 8724753 351068 15.26 585.2 8833907 351887 15.709 588.2 8825450 355897 18.618 595.4 8882536 333652 15.397 596.7 8911627 336662 17.384 592 8916377 344441 27.92 589.4 8955472 406510 14.555 593.9 9034368 322222 18.684 595.2 9079246 318184 16.639 598.6 9123848 366989 20.169 603.5 9175181 357334 16.901 606.5 9238576 380085 21.47 607.8 9270505 373279 16.542 609.6 9338876 368611 16.98 610.9 9352650 382600 20.091 607.9 9348494 352686 16.583 604.6 9376027 354740 18.761 603.6 9410758 363468 28.795 604.5 9478531 424946 20.473 606.348 9540335 332797

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. EE 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+ (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.) PC+ Retail Sales index 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 %. :005, tet-atio fo CP much of the variation in the retailer's revenue. Round to one decimal place as needed.) s nificant the tato fr PC ' sign foa , and het o or Retail Salesindex significant.lto ksket ese anat es " account for 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. EE 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+ (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.) PC+ Retail Sales index 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 %. :005, tet-atio fo CP much of the variation in the retailer's revenue. Round to one decimal place as needed.) s nificant the tato fr PC ' sign foa , and het o or Retail Salesindex significant.lto ksket ese anat es " account for