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the Question are in one pic
the other pictures are the options for them
Required information Excel Analytics 5-51: Interpretation of Cost Estimation Results: Visualization [The following information applies to the questions displayed below]. Waveney DIY Centers (WDC) operates a few dozen stores in the eastern United States. The stores are popular with home remodelers, contractors, and do-lt-yourself customers. The managers at Waveney are interested in understanding what drives costs as well as getting better cost estimates when planning new stores. The area manager for the Southeast Region is interested in new data analysis approaches to management and offered to run a test using data from the 14 stores in the region. The initial thoughts of the managers and the financial analysts in the region were that two primary factors drove store costs: store area (square footage) and revenue. The following data were collected from the most recent year of operations (revenues and costs in thousands of dollars): Results from a regression of store costs on revenues are as follows: Equationt 5 tofe cost =$2,591.22+(64.61 Revenue ) Statistical data correlation coelfiesent 2 0.999 0.998 Results from a regression of store costs on store area are as follows: Tquation: cverbead =$3,025.02+(50.176= Area ) statiaticel data Correlatios cootfielent n2 0.863 Results from a multiple regression of store costs on revenues and store area are as follows: Tquation: atore cost =52,298.01+(60.00 * Mevenses )+(50.017. Area ) Statiotical data correlation coeftielent. Mdjunted A2 33. Save your progress by choosing Save As from the File menu. You may now answer all questions Questions 1. Which of the following statements regarding the store cost estimates is accurate? We would have 2. Which of thefollowing is not a reason why we would have more confidence in the 50,000 -square foot cost estimate than the 35,000 square foot cost estimate? 3. Which of the following conclusions can be drawn from these simple regression analyses? 4. What are the estimated store costs based on the multiple regression analysis using both revenue and square footage? 5. What are the estimated store costs, assuming $19,000,000 of revenue? Answers We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. ason whin we would havo maro We would be equally confident in the $19,000,000-revenue estimate and the $30,000,000-revenue estimate. We would be more confident in the $19,000,000-revenue estimate than the $30,000,000-revenue estimate because revenue of $30,000,000 is outside of the relevant range. We would be more confident in the $30,000,000-revenue estimate than the $19,000,000-revenue estimate because revenue of $19,000,000 is outside of the relevant range. zestions Answers its regarding the store cost estimates is We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. eason why we would have more jot cost estimate than the 35,000 - ans can be r zwn from these simnle The 35,000 -square foot estimate is outside of the relevant range. jsts bas The 35,000-square foot location carries a smaller range of products than a typical store. The 35,000-square foot location may have a different customer base that that of a typical store. The 35,000 -square foot location is smaller than the 50,000 -square foot location. jestions Answers its regarding the store cost estimates is We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. eason why we would have more sot cost estimate than the 35,000 - ans can be drawn from these simple ists based of the multinle rearesesinn iquare fs Revenue is more highly correlated with store costs than square footage. Square footage is more highly correlated with store costs than revenue. Revenue and square footage are equally correlated with store costs. Neither revenue nor square footage are highly correlated with store costs. Its regarding the store cost estimates is We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. eason why we would have more sot cost estimate than the 35,000 - ns can be drawn from these simple ists based on the multiple regression quare footage? sts, ass $10,747,000$11,825,020$14,548,880$14,865,220 Required information Excel Analytics 5-51: Interpretation of Cost Estimation Results: Visualization [The following information applies to the questions displayed below]. Waveney DIY Centers (WDC) operates a few dozen stores in the eastern United States. The stores are popular with home remodelers, contractors, and do-lt-yourself customers. The managers at Waveney are interested in understanding what drives costs as well as getting better cost estimates when planning new stores. The area manager for the Southeast Region is interested in new data analysis approaches to management and offered to run a test using data from the 14 stores in the region. The initial thoughts of the managers and the financial analysts in the region were that two primary factors drove store costs: store area (square footage) and revenue. The following data were collected from the most recent year of operations (revenues and costs in thousands of dollars): Results from a regression of store costs on revenues are as follows: Equationt 5 tofe cost =$2,591.22+(64.61 Revenue ) Statistical data correlation coelfiesent 2 0.999 0.998 Results from a regression of store costs on store area are as follows: Tquation: cverbead =$3,025.02+(50.176= Area ) statiaticel data Correlatios cootfielent n2 0.863 Results from a multiple regression of store costs on revenues and store area are as follows: Tquation: atore cost =52,298.01+(60.00 * Mevenses )+(50.017. Area ) Statiotical data correlation coeftielent. Mdjunted A2 33. Save your progress by choosing Save As from the File menu. You may now answer all questions Questions 1. Which of the following statements regarding the store cost estimates is accurate? We would have 2. Which of thefollowing is not a reason why we would have more confidence in the 50,000 -square foot cost estimate than the 35,000 square foot cost estimate? 3. Which of the following conclusions can be drawn from these simple regression analyses? 4. What are the estimated store costs based on the multiple regression analysis using both revenue and square footage? 5. What are the estimated store costs, assuming $19,000,000 of revenue? Answers We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. ason whin we would havo maro We would be equally confident in the $19,000,000-revenue estimate and the $30,000,000-revenue estimate. We would be more confident in the $19,000,000-revenue estimate than the $30,000,000-revenue estimate because revenue of $30,000,000 is outside of the relevant range. We would be more confident in the $30,000,000-revenue estimate than the $19,000,000-revenue estimate because revenue of $19,000,000 is outside of the relevant range. zestions Answers its regarding the store cost estimates is We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. eason why we would have more jot cost estimate than the 35,000 - ans can be r zwn from these simnle The 35,000 -square foot estimate is outside of the relevant range. jsts bas The 35,000-square foot location carries a smaller range of products than a typical store. The 35,000-square foot location may have a different customer base that that of a typical store. The 35,000 -square foot location is smaller than the 50,000 -square foot location. jestions Answers its regarding the store cost estimates is We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. eason why we would have more sot cost estimate than the 35,000 - ans can be drawn from these simple ists based of the multinle rearesesinn iquare fs Revenue is more highly correlated with store costs than square footage. Square footage is more highly correlated with store costs than revenue. Revenue and square footage are equally correlated with store costs. Neither revenue nor square footage are highly correlated with store costs. Its regarding the store cost estimates is We would have low confidence in both the $19,000,000-revenue estimate and the $30,000,000-revenue estimate because they are both outside of the relevant range. eason why we would have more sot cost estimate than the 35,000 - ns can be drawn from these simple ists based on the multiple regression quare footage? sts, ass $10,747,000$11,825,020$14,548,880$14,865,220
