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A: Current Price of Shoes = 4,499.50 Annual Sales = 24 Cost of the Shoes = 3,450.00
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- 21. Consider a firm subject to quarter-to-quarter variation in its sales. Suppose that the following equation was estimated using quarterly data for the period 2011–2018 (the time variable goes from 1 to 32). The variables D1, D2, and D3 are, respectively, dummy variables for the first, second, and third quarters (e.g., D1 is equal to 1 in the first quarter and 0 otherwise). Qt =a+bt+c1D1+c2D2+c3D3 The results of the estimation are presented here: a. Calculate the intercept in each of the four quarters. What do these values imply? b. Use this estimated equation to forecast sales in the fourth quarter of 2019.A company has established that the relationship between the sales price for one of its products and the quanlity sokld per month is approximalely p=75-0. 1D (D is the demand or quantity sold per month and p is the price in dollars). The fixed cost is $1,000 per month and the variable cost is $30 per unit produced. a. What is the maximum profit per month for this product? b. What is the range of profitable demand during a month? a. The maximum profit per month for this product is $. (Round to the nearest dollar.) b. The range of profitable demand during a month is from units to units. (Round up the lower limit and down the upper limit to the nearest whole number.)5. Exercise 5.5 A firm experienced the demand shown in the following table. Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w = 0.9 forecasts may be begun by assuming Y t+ F Yt •) and w = 0.3 ). (Note: The exponential smoothing Moving Average Actual Demand (5-year) (3-year) Exponential Smoothing (W = 0.9) Year (W = 0.3) 2000 900 2001 885 900 900 2002 875 2003 870 887 ▼ 2004 870 877 ▼ 2005 875 880 872 Y 2006 885 875 872 2007 900 875 877 2008 920 880 887 - 2009 945 890 902 Y 2010 905 922 The following table shows the square errors, (Y; - T1-) , for forecasts from 2005 through 2009. Fill the table by calculating the root mean square error (RMSE) for each of the methods. Square Error Exponential Smoothing Moving Average Year (5-year) (3-year) (W = 0.9) (W = 0.3) 2005 25 9 25 25 2006 100 169 100 36 2007 625 529 256 361 2008 1,600 1,089 484 1,089 2009 3,025 1,849 729 2,304 RMSE Based on the RMSE criterion,…
- Question 3 The number of tons of brake assemblies received at an auto parts distribution center last month was 625. The forecast tonnage was 650 for last month. The company uses a simple exponential smoothing model with a smoothing constant of 0.46 to develop its forecasts. What will be the company's forecast for the next month? Add your answerIn the past four years, the annual returns of one company’s stockare 12%, 18%, and –14%, and 7%.a) What is the geometric average return? b) What is the arithmetic average of the returns? c) According to an economist’ forecast on the Year 2020, the probabilities of repeatingthe performances of the former four years are 30%, 30%, 20%, and 20%, respectively.What is the expected return of the stock in the Year 2020A firm keeps a record of sales and prices over the past seven months, resulting in the following table: Price (ZMW/ton) Sales (tons) Nov. 1985 7.5 84.5 Dec. 8.0 82.0 Jan. 1986 8.0 84.0 Feb. 7.2 92.0 March 7.0 95.0 April 8.0 92.0 May 8.5 91.5 Use these observations to estimate demand as a linear function of both price and time. Further, utilise this function to estimate demand for the following month, on the assumption that: (a) price remains unchanged, (b) price increases to ZMW9/ton. Hence estimate the price elasticity of demand between these prices and find the price which would maximise sales revenue. Given the nature of the observations, comment on any difficulties in interpreting your results for decision-making purposes.
- Bell Greenhouses has estimated its monthly demand for potting soil to be the following: Assume this trend factor is expected to remain stable in the foreseeable future. The following table contains the monthly seasonal adjustment factors, which have been estimated using actual sales data from the past five years: MONTH ADJUSTMENT FACTOR (%) March +2 June +15 August +10 December −12 a. Forecast Bell Greenhouses’ demand for potting soil in March, June, August, and December 2007. Answer b. If the following table shows the forecasted and actual potting soil sales by Bell Greenhouses for April in five different years, determine the seasonal adjustment factor to be used in making an April 2008 forecast. YEAR FORECAST ACTUAL 2007 500 515 2006 452 438 2005 404 420 2004 356 380 2003 308 320As the selling price of a product increases, demand decreases. Fewer people will buy a product if it is more expensive. This is a very important concept. The demand equation for new iSmarts is given by 0.025x , where p is the unit p = 100 price (in dollars) of the iSmart and x is the number of units produced. We can determine the total revenue (money earned for selling x iSmarts) by multiplying the number of items we sell, x and the price p. The total revenue obtained by producing and selling x iSmarts is given by = xp 100x – 0.025x². It makes sense that this function is quadratic. The company can price the iSmarts so high that no one will buy them and the revenue will be zero. The can also set the price to $0, but the revenue will still be $0, no matter how many iSmarts they "sell". In between, the graph is a parabola opening downward. Determine prices p that would keep revenue above 6530 dollars. Give your answers to 2 decimal places. The revenue will be above 6530 if price is…Imagine that you work for the maker of a leading brand of low-calorie, frozen microwavable food that estimates the following demand equation for its product using data from 26 supermarkets around the country for the month of April. Note: The following is a regression equation. Standard errors are in parentheses for the demand for widgets. QD = - 5200 - 42P + 20PX + 5.2I + 0.20A + 0.25M (2.002) (17.5) (6.2) (2.5) (0.09) (0.21) R2 = 0.55 n = 26 F = 4.88 Your supervisor has asked you to compute the elasticities for each independent variable. Assume the following values for the independent variables: Q = Quantity demanded of 3-pack units P (in cents) = Price of the product = 500 cents per 3-pack unit PX (in cents) = Price of leading competitor's product = 600 cents per 3-pack unit I (in dollars) = Per capita income of the standard metropolitan statistical area (SMSA) in…
- Director Very Busy needs to allocate time this week for office appointments, so he needs to forecast the number of employees who will seek appointments. The director has gathered the following time series data recently Period Employee Appointments 4 weeks ago 95 3 weeks ago 80 2 week ago 65 last week 506-5 Given: Consider the following time series data. week 1 2 3 4 5 6 Value 18 13 16 11 17 14 Week Time Series Value Forecast 1 18 2 13 3 16 15.6667 4 11 13.3333 5 17 14.6667 6 14 14 MSE = 11.889 Forecast for week 7 = 14 Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. If required, round your answers to two decimal places. Week Time SeriesValue Forecast 1 18 2 13 3 16 4 11 5 17 6 14 MSE: The forecast for week 7: Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE?Calculate the percentage change of the variable in each of the following cases. Then calculate the percentage change if the movement is occurring in the opposite direction, with what was the final value now the initial value and vice versa. Now calculate a comparable percentage change using the average of the initial and ending values. Express all three changes in absolute value form without positive or negative signs and as whole numbers (i.e. 67%, not 66.6%). a. A fast-food restaurant, which originally sold hamburgers at a price of $5, increases their price to $6. The absolute value of this percentage change is %, and the absolute value of the percentage change calculated using the average of the two values is %, the absolute value of the percentage change in the opposite direction is %. b. The number of autos sold monthly at a car dealership drops from 400 to 300. The absolute value of this percentage change is %, the absolute value of the percentage change in the opposite direction…