4.99 See Answer

Question: Demand estimation for brand-name consumer products

Demand estimation for brand-name consumer products is made difficult by the fact that managers must rely on proprietary data. There simply is not any publicly available data which can be used to estimate demand elasticities for brand-name orange juice, frozen entrees, pies, and the like--and with good reason. Competitors would be delighted to know profit margins across a broad array of competing products so that advertising, pricing policy, and product development strategy could all be targeted for maximum benefit. Product demand information is valuable and jealously guarded. To see the process that might be undertaken to develop a better understanding of product demand conditions, consider the hypothetical example of Mrs. Smyth's Inc., a Chicago-based food company. In early 2008, Mrs. Smyth's initiated an empirical estimation of demand for its gourmet frozen fruit pies. The firm is formulating pricing and promotional plans for the coming year, and management is interested in learning how pricing and promotional decisions might affect sales. Mrs. Smyth's has been marketing frozen fruit pies for several years, and its market research department has collected quarterly data over two years for six important marketing areas, including sales quantity, the retail price charged for the pies, local advertising and promotional expenditures, and the price charged by a major competing brand of frozen pies. Statistical data published by the U.S. Census Bureau (http://www.census.gov) on population and disposable income in each of the six Metropolitan Statistical Areas were also available for analysis. It was therefore possible to include a wide range of hypothesized demand determinants in an empirical estimation of fruit pie demand. These data appear in Table 5.3.
Demand estimation for brand-name consumer products is made difficult by the fact that managers must rely on proprietary data. There simply is not any publicly available data which can be used to estimate demand elasticities for brand-name orange juice, frozen entrees, pies, and the like--and with good reason. Competitors would be delighted to know profit margins across a broad array of competing products so that advertising, pricing policy, and product development strategy could all be targeted for maximum benefit. Product demand information is valuable and jealously guarded. To see the process that might be undertaken to develop a better understanding of product demand conditions, consider the hypothetical example of Mrs. Smyth's Inc., a Chicago-based food company. In early 2008, Mrs. Smyth's initiated an empirical estimation of demand for its gourmet frozen fruit pies. The firm is formulating pricing and promotional plans for the coming year, and management is interested in learning how pricing and promotional decisions might affect sales. Mrs. Smyth's has been marketing frozen fruit pies for several years, and its market research department has collected quarterly data over two years for six important marketing areas, including sales quantity, the retail price charged for the pies, local advertising and promotional expenditures, and the price charged by a major competing brand of frozen pies. Statistical data published by the
U.S. Census Bureau (http://www.census.gov) on population and disposable income in each of the six Metropolitan Statistical Areas were also available for analysis. It was therefore possible to include a wide range of hypothesized demand determinants in an empirical estimation of fruit pie demand. These data appear in Table 5.3.
The following regression equation was fit to these data:
Qit = b0 + b1Pit + b2Ait + b3PXit + b4Yit + b5Popit + b6Tit + uit
Q is the quantity of pies sold during the  quarter; P is the retail price in dollars of Mrs. Smyth's frozen pies; A represents the dollars spent for advertising; PX is the price, measured in dollars, charged for competing premium-quality frozen fruit pies; Y is the median dollars of disposable income per household; Pop is the population of the market area; T is the trend factor (2006-1 = 1, . . . , 2007-4 = 8); and i is a residual (or disturbance) term. The subscript i indicates the regional market from which the observation was taken, whereas the subscript t represents the quarter during which the observation occurred. Least squares estimation of the regression equation on the basis of the 48 data observations (eight quarters of data for each of six areas) resulted in the estimated regression coefficients and other statistics given in Table 5.4.
a. Describe the economic meaning and statistical significance of each individual independent variable included in the Mrs. Smyth's frozen fruit pie demand equation.
b. Interpret the coefficient of determination (R2) for the Mrs. Smyth's frozen fruit pie demand equation.
c. Use the regression model and 2007-4 data to estimate 2008-1-unit sales in the Washington-Arlington-Alexandria   market.
d. To illustrate use of the standard error of the estimate statistic, derive the 95 percent and 99 percent confidence intervals for 2008-1-unit sales in the Washington-Arlington- Alexandria market.


Demand estimation for brand-name consumer products is made difficult by the fact that managers must rely on proprietary data. There simply is not any publicly available data which can be used to estimate demand elasticities for brand-name orange juice, frozen entrees, pies, and the like--and with good reason. Competitors would be delighted to know profit margins across a broad array of competing products so that advertising, pricing policy, and product development strategy could all be targeted for maximum benefit. Product demand information is valuable and jealously guarded. To see the process that might be undertaken to develop a better understanding of product demand conditions, consider the hypothetical example of Mrs. Smyth's Inc., a Chicago-based food company. In early 2008, Mrs. Smyth's initiated an empirical estimation of demand for its gourmet frozen fruit pies. The firm is formulating pricing and promotional plans for the coming year, and management is interested in learning how pricing and promotional decisions might affect sales. Mrs. Smyth's has been marketing frozen fruit pies for several years, and its market research department has collected quarterly data over two years for six important marketing areas, including sales quantity, the retail price charged for the pies, local advertising and promotional expenditures, and the price charged by a major competing brand of frozen pies. Statistical data published by the
U.S. Census Bureau (http://www.census.gov) on population and disposable income in each of the six Metropolitan Statistical Areas were also available for analysis. It was therefore possible to include a wide range of hypothesized demand determinants in an empirical estimation of fruit pie demand. These data appear in Table 5.3.
The following regression equation was fit to these data:
Qit = b0 + b1Pit + b2Ait + b3PXit + b4Yit + b5Popit + b6Tit + uit
Q is the quantity of pies sold during the  quarter; P is the retail price in dollars of Mrs. Smyth's frozen pies; A represents the dollars spent for advertising; PX is the price, measured in dollars, charged for competing premium-quality frozen fruit pies; Y is the median dollars of disposable income per household; Pop is the population of the market area; T is the trend factor (2006-1 = 1, . . . , 2007-4 = 8); and i is a residual (or disturbance) term. The subscript i indicates the regional market from which the observation was taken, whereas the subscript t represents the quarter during which the observation occurred. Least squares estimation of the regression equation on the basis of the 48 data observations (eight quarters of data for each of six areas) resulted in the estimated regression coefficients and other statistics given in Table 5.4.
a. Describe the economic meaning and statistical significance of each individual independent variable included in the Mrs. Smyth's frozen fruit pie demand equation.
b. Interpret the coefficient of determination (R2) for the Mrs. Smyth's frozen fruit pie demand equation.
c. Use the regression model and 2007-4 data to estimate 2008-1-unit sales in the Washington-Arlington-Alexandria   market.
d. To illustrate use of the standard error of the estimate statistic, derive the 95 percent and 99 percent confidence intervals for 2008-1-unit sales in the Washington-Arlington- Alexandria market.

The following regression equation was fit to these data: Qit = b0 + b1Pit + b2Ait + b3PXit + b4Yit + b5Popit + b6Tit + uit Q is the quantity of pies sold during the quarter; P is the retail price in dollars of Mrs. Smyth's frozen pies; A represents the dollars spent for advertising; PX is the price, measured in dollars, charged for competing premium-quality frozen fruit pies; Y is the median dollars of disposable income per household; Pop is the population of the market area; T is the trend factor (2006-1 = 1, . . . , 2007-4 = 8); and i is a residual (or disturbance) term. The subscript i indicates the regional market from which the observation was taken, whereas the subscript t represents the quarter during which the observation occurred. Least squares estimation of the regression equation on the basis of the 48 data observations (eight quarters of data for each of six areas) resulted in the estimated regression coefficients and other statistics given in Table 5.4.
Demand estimation for brand-name consumer products is made difficult by the fact that managers must rely on proprietary data. There simply is not any publicly available data which can be used to estimate demand elasticities for brand-name orange juice, frozen entrees, pies, and the like--and with good reason. Competitors would be delighted to know profit margins across a broad array of competing products so that advertising, pricing policy, and product development strategy could all be targeted for maximum benefit. Product demand information is valuable and jealously guarded. To see the process that might be undertaken to develop a better understanding of product demand conditions, consider the hypothetical example of Mrs. Smyth's Inc., a Chicago-based food company. In early 2008, Mrs. Smyth's initiated an empirical estimation of demand for its gourmet frozen fruit pies. The firm is formulating pricing and promotional plans for the coming year, and management is interested in learning how pricing and promotional decisions might affect sales. Mrs. Smyth's has been marketing frozen fruit pies for several years, and its market research department has collected quarterly data over two years for six important marketing areas, including sales quantity, the retail price charged for the pies, local advertising and promotional expenditures, and the price charged by a major competing brand of frozen pies. Statistical data published by the
U.S. Census Bureau (http://www.census.gov) on population and disposable income in each of the six Metropolitan Statistical Areas were also available for analysis. It was therefore possible to include a wide range of hypothesized demand determinants in an empirical estimation of fruit pie demand. These data appear in Table 5.3.
The following regression equation was fit to these data:
Qit = b0 + b1Pit + b2Ait + b3PXit + b4Yit + b5Popit + b6Tit + uit
Q is the quantity of pies sold during the  quarter; P is the retail price in dollars of Mrs. Smyth's frozen pies; A represents the dollars spent for advertising; PX is the price, measured in dollars, charged for competing premium-quality frozen fruit pies; Y is the median dollars of disposable income per household; Pop is the population of the market area; T is the trend factor (2006-1 = 1, . . . , 2007-4 = 8); and i is a residual (or disturbance) term. The subscript i indicates the regional market from which the observation was taken, whereas the subscript t represents the quarter during which the observation occurred. Least squares estimation of the regression equation on the basis of the 48 data observations (eight quarters of data for each of six areas) resulted in the estimated regression coefficients and other statistics given in Table 5.4.
a. Describe the economic meaning and statistical significance of each individual independent variable included in the Mrs. Smyth's frozen fruit pie demand equation.
b. Interpret the coefficient of determination (R2) for the Mrs. Smyth's frozen fruit pie demand equation.
c. Use the regression model and 2007-4 data to estimate 2008-1-unit sales in the Washington-Arlington-Alexandria   market.
d. To illustrate use of the standard error of the estimate statistic, derive the 95 percent and 99 percent confidence intervals for 2008-1-unit sales in the Washington-Arlington- Alexandria market.

a. Describe the economic meaning and statistical significance of each individual independent variable included in the Mrs. Smyth's frozen fruit pie demand equation. b. Interpret the coefficient of determination (R2) for the Mrs. Smyth's frozen fruit pie demand equation. c. Use the regression model and 2007-4 data to estimate 2008-1-unit sales in the Washington-Arlington-Alexandria market. d. To illustrate use of the standard error of the estimate statistic, derive the 95 percent and 99 percent confidence intervals for 2008-1-unit sales in the Washington-Arlington- Alexandria market.





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Table 5.3 Mrs Smyth's Gourmet Frozen Fruit Pie Regional Market Demand Data, 2006-1 to 2007-4 Advertising Competitors' Time Year-Quarter Unit Sales (Q) Price ($) Expenditures ($) Price ($) Income ($) Population Variable Atlanta-Sandy Springs-Marietta, GA 2006-1 193 334 6.90 15827 7.40 48421 5055 856 1 2006-2 170041 7.79 20819 5.18 49038 5091361 2 5 127115 5 163 120 5 199378 5 235 891 5 272 660 2006-3 247 709 6.21 14 062 5.65 49 663 2006-4 183 259 7.29 16973 6.60 50 296 4 2007-1 282 118 6.87 18815 6.81 50938 2007-2 203 396 6.46 14176 5.22 51587 2007-3 167 447 7.17 17030 5.59 52245 7 2007-4 361 677 5.72 14456 6.21 52911 5 309 688 8. Chicago-Naperville-Joliet, IL-IN-WI 2006-1 401 805 6.57 27 183 5.34 50456 9509 448 1 2006-2 412312 6.62 27 572 6.56 51 100 9526 087 2 2006-3 321 972 7.82 34 367 6.23 51751 9542755 2006-4 445 236 6.57 26895 6.47 52411 9559452 4 2007-1 479713 6.91 30539 5.75 53 079 9576178 2007-2 459 379 6.48 26679 5.20 53756 9592933 6. 2007-3 444040 6.44 26607 5.66 54441 9609718 7 2007-4 376046 7.79 32760 5.23 55 136 9626532 8. Table 5.3 Mrs Smyth's Gourmet Frozen Fruit Pie Regional Market Demand Data, 2006-1 to 2007-4 Time Advertising Expenditures (S) Competitors' Year-Quarter Unit Sales (Q) Price ($) Price ($) Income ($) Population Variable Dallas-Fort Worth-Arlington, TX 2006-1 255 203 7.07 19880 7.46 44 249 5952 784 1 2006-2 270881 6.60 19151 6.69 44813 5986 874 2 2006-3 330 271 6.07 15743 6,45 45 385 6021 160 3 2006-4 313485 6.54 17512 5.44 45963 6055642 4 2007-1 311 500 6.30 16984 5.66 46549 6090 322 5 2007-2 370780 5.81 15698 6.62 47 143 6125 200 2007-3 152338 8.00 22057 7.43 47744 6160 278 2007-4 320804 6.69 17460 6.83 48353 6195 557 8 Los Angeles-Long Beach-Santa Ana, CA 2006-1 738760 6.21 42925 5.93 53929 13030242 1 2006- 707015 7.14 50 299 7.20 54617 13057 136 2006-3 699 051 5.43 37364 5.39 55313 13 084 085 2006-4 628838 7.30 50602 4.93 56018 13111090 2007-1 631934 7.60 53 562 6.26 56732 13138 151 5 2007-2 651 162 7.24 48911 6.02 57456 13 165 267 6 2007-3 765 124 7.06 49422 7.43 58 188 13192440 7 2007-4 741364 6.19 44061 6.82 58930 13219669 Minneapolis-St. Paul-Bloomington, MN-WI 2006-1 291773 5.78 13896 6.18 58959 3176146 2006-2 153018 6.84 27429 5.06 59711 3184576 2 2006-3 574 486 6.42 31631 7.17 60472 3193 029 3 2006-4 75 396 7.56 39176 4.90 61243 3201 504 4 2007-1 590 190 5.61 33538 5.53 62024 3210002 5 288 112 53643 62815 3218 522 3227 065 3235 630 2007-2 7.58 5.51 6. 2007-3 276619 7.58 60 284 5.84 63616 7 2007-4 522446 5.65 53 595 6.48 64 427 Washington-Arlington-Alexandria, DC-VA-MD-wv 395314 2006-1 6.26 22626 7.02 48717 5 297 098 1 2006-2 436 103 5.75 22697 6.83 49338 5318031 2 2006-3 336338 6.86 25475 4.85 49967 5339 048 3. 2006-4 451321 6.43 25734 6.75 50 604 5360147 2007-1 352 181 6.49 23777 6.68 51 249 5 381 330 2007-2 317322 7.58 27544 5.20 51 903 5402 596 2007-3 422455 6.17 23852 5.20 52 565 5423 947 7 2007-4 290 963 7.95 30487 5.69 53 235 5445 382 8. Average 391917 6.74 29204 6.09 53114 7 087461 Table 5.4 Estimated Demand Function for Mrs Smyth's Gourmet Frozen Fruit Pies t Statistic (4) = (2)/(3) Standard Error Variable (1) Coefficient (2) of Coefficient (3) Intercept 529774 271331 1.95 Price (P) - 122607 16422 -7.47 Advertising (A) 5.838 1.65 3.54 Competitor Price (PX) 29 867 13449 2,22 Income (Y) 2.043 3.762 0.54 Population (Pop) Time (7) 0.030 0.004 7,50 2815 4539 0.62 Coefficient of determination = R? = 87.1% Corrected coefficient of determination = R? = 85.2% %3D F statistic = 45.16 Standard error of the estimate = SEE = 67584


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> Tempe Chemicals refines a variety of chemicals for cleaning products. The following data are from the company’s Greenville plant: Work in Process, May 1 2,300,000 gallons Direct material 100% complete Conversion costs 40% complete Units started in proces

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> For the month of September, the Wilber Pickle Company had cost per equivalent unit of $0.80 for materials (pickles, vinegar, spices, etc.) and $.90 for conversion costs (labor and overhead). At Wilber Pickle, units are measured in quarts. The company beg

> The McMillian Tire Company produces tires used on small trailers. The month of June ended with 600 tires in process, 90 percent complete as to direct materials, and 50 percent complete as to conversion costs; 2,000 tires were transferred to finished good

> In each case below, fill in the missing amount. Work in Process, October 1 Units started during October Units completed during October Work in Process, October 31 Case #1 5,000 gallons ? 15,000 gallons 2,500 gallons Work in Process, March 1 Units sta

> Write a paragraph explaining the calculation of the cost per equivalent unit (i.e., explain what goes in the numerator and what goes in the denominator of the calculation). Be sure to explain why the denominator may be different for the cost per equivale

> Techno Enterprises is a manufacturer of microchips (referred to as chips). Its production process is complex and involves more than 100 steps, starting with production of small, round silicon wafers and ending with chips being put into individual package

> Woodinville Cement uses a process costing system. In 2017, the company produced and sold 100,000 bags of cement and incurred the following costs: The current selling price is $4 per unit, and the profit for 2017 was ($4 × 100,000) â&#

> The Amigo Glass Company manufactures glass for sliding glass doors. At the start of August 2,000 units were in process. During August, 15,000 units were complete and 3,000 units were in process at the end of August. These in-process units were 100 percen

> Consider a large manufacturing company like Boeing that rewards its sales force with bonuses based on sales. For this purpose, should the company record sales when orders are placed or, to be consistent with GAAP, wait until orders are delivered?

> The Simon Fishing Company processes salmon for various distributors. The two departments involved are Cleaning and Packing. The next table summarizes the data related to pounds of salmon processed in the Cleaning Department during June: All materials ar

> At the start of November, Penco Refinery had Work in Process inventory consisting of 4,000 units that were 90 percent complete with respect to materials and 50 percent complete with respect to conversion costs. The cost of the units was $43,000 ($30,000

> At the start of July, the Classic Car Wax Company had beginning Work in Process of 2,500 units that were 90 percent complete with respect to material and 45 percent complete with respect to conversion costs. The cost of the units was $7,000 ($5,000 of ma

> The Western Beverage Company is marketing a new product, Tech-Tonic Sports Drink Syrup. The product sells for $16 per gallon, and in recent months the company has had sales of more than 525,000 gallons per month. Consumers mix 1 part syrup with 5 parts w

> Jensen PVC, Inc., produces polyvinyl chloride (PVC) irrigation pipes. In 2017, the cost of producing a foot of pipe was $0.30, and the selling price was $0.39 per foot. In 2018, production costs increased to $0.40 per foot, although the selling price rem

> What is the difference between product and period costs?

> As companies move to computer-controlled manufacturing systems, what happens to the mix of product costs (direct material, direct labor, and manufacturing overhead)?

> What is a job cost sheet? What information does it contain?

> Discuss an important characteristic of a good overhead allocation base.

> Why do companies apply overhead to jobs using a predetermined (budgeted) overhead rate instead of applying actual overhead to jobs?

> Bob Stevens is taking Managerial Accounting at State University next term and asked his friend, Summer Adams, who has already taken the course, to explain its focus. “Are we going to learn more about balance sheets and income statements? Is it just an ex

> Identify the two most common types of product costing systems and discuss the manufacturing environments associated with each system.

> What is the difference between manufacturing and nonmanufacturing costs?

> Would an unexpected increase in sales and production result in underapplied or overapplied overhead? Explain.

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