Todd uses the quadratic regression model to determine the predictive average unit cost of baseballs produced in his production facility. After determining predictive average costs at multiple unit batch size amounts in millions, he now wants to know what the output level that maximizes his costs would be. Given b₁ = -0.3904 and b₂ = 0.0247, what is the level that will maximize his average cost in units? Multiple Choice 7.90 million units 7.2 million units 2.8 million units 20.40 million units

The University of Miami bookstore stocks textbooks in preparation for sales each semester. It normally relies on departmental forecasts and preregistration records to determine how many copies of a text are needed. Preregistration shows 85 operations management students enrolled, but bookstore manager Vaidy Jayaraman has second thoughts, based on his intuition and some historical evidence. Vaidy believes that the distribution of sales may range from 65 to 85 units, according to the following probability model: Demand Probability 65 0.05 Stock 65 70 75 80 85 70 0.25 This textbook costs the bookstore $82 and sells for $107. Any unsold copies can be returned to the publisher, less a restocking fee and shipping, for a net refund of $30. a) Based on the given information, Vaidy's conditional profits table for the bookstore is: Demand 75 P=0.30 65 P = 0.05 0000 70 P = 0.25 75 0.30 ☐☐☐☐ 80 0.15 00000 80 85 P = 0.15 P = 0.25 30000 85 0.25 ☐☐☐☐

The following shows five months forecast demand and the corresponding actual demand for certain product.

A shop is planning an order for a popular Christmas festive season product. Demand for the product usually starts from first week of December till first week of January and reduces sharply thereafter. For this reason, and to stimulate sales for leftovers, the product is sold at a significantly reduced price from the second week of January to the fourth week of January. Any leftover after the fourth week of January goes waste. The table below gives past data on total demand for the period from first week of December to first week of January, and from second week of January to fourth week of January, together with their respective chances of occurrence. The product can be purchased at a wholesale price of GHS60 per unit for a pack containing 600 products, GHS57 per unit for a pack containing 800 products, and GHS52 per unit for a pack containing 1000 products. The shop plans to sell the product for GHS80 per unit from first week of December to first week of January, and at a reduced…

Andrew Thomas, a sandwich vendor at hard rock café annual rockfest, created a table of conditional values for the various alternatives (stocking decision) and state of nature (size of crowd) Alternatives Large stock average stock small stock probabilities associated with states of nature are Big Big 35000 20000 10500 0.3 State of nature (demand) Average Small 15000 10000 8000 Average 0.5 determine the alternative that gives Andrew the greatest expected monetary value (EMV) compute the extpected value of perfect information (EVPI). Small -3000 5000 3000 0.2

Mop and Broom Manufacturing has tracked the number of units sold of their most popular mop over the past 24 months. This is shown. Month 1 2 3 4 5 6 7 8 Sales Month 238 249 254 267 277 282 402 296 9 10 11 12 13 14 15 16 Sales 300 327 346 349 353 364 373 363 Month + 17 18 19 20 21 22 23 24 Sales 361 381 375 383 384 401 409 403 a. Develop a linear trend line for the data. (Round your answers to 2 decimal places, e.g.,25.28.) Sales = (month) b. Compute a correlation coefficient for the data and evaluate the strength of the linear relationship. (Round your answer to 2 decimal places, the tolerance is +/-0.01.) Correlation coefficient is . It indicates linear relationship. (Use not rounded amounts to answer this question.) c. Using the linear trend line equation, develop a forecast for the next period,month 25. (Round your answer to 2 decimal places, the tolerance is +/-0.01. Do not round intermediate results used to achieve this answer.) Forecast for month 25 =

Demand is forecasted using the following regression equation: D = 100 + 15(t) where “t” is a specified future time period. The current time period is 0 (t=0). The machines used to make this product have a capacity of 50 units/time period. The company currently has 4 machines yielding a total capacity of 200 units/time period. Due to technology changes, the current equipment will become obsolete at the end of period 15. If a new machine costs $2000, and the net revenue earned per unit is $10, how many machines should be added between today (t=0) and the end of period 15?

A home improvement store sells boxwood shrubs during the spring planting season. Each boxwood sells for $35 and it costs the store $13 to purchase and transport each boxwood from its supplier. At the end of the season, any remaining boxwoods are sold to a local landscaper at a deep discount for $5/boxwood. In the upcoming season, demand for boxwoods is forecast to be normally distributed with a mean of 1,000 and a standard deviation of 200 (you may assume demand is continuous). If the store can only order once at the beginning of the season, how many boxwoods should be stocked?

Fit a straight line trend to the following data on demand of steel ingots (in millions) and project the demand for the year 2009.Year2002200320042005200620072008Demand8084909398100104

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