Ruby's Craft & Design is a U.S.-based mass producer of Halloween costumes (think pirates, nurses, zombies, and so on.) Each year, the company produces hundreds of thousands of pre-packaged "all in one costume kits, which are sold to Halloween supply stores around the country, who in turn sell the kits to consumers at a marked-up price. Each year, the company's managers tailor their production plan in an attempt to capitalize on any cultural trends that may result in increased demand for costumes reflecting specific characters or themes. In January 2021, a particularly amusing photograph of Vermont senator Bernie Sanders appearing to look quite unimpressed at the Presidential Inauguration ceremony became an overnight internet sensation and the subject of countless memes. Management immediately speculated that there might be a surge in demand for Bernie Sanders costumes for Halloween 2021. Through a hurried development process involving the company's design and production teams, management derived rough estimates for the costume kit's potential profit. Management estimates that the cost of materials and labor to produce each Bernie Sanders costume kit would be uniformly distributed between $8 and $12. The sales department estimates they could sell the kits to Halloween stores for approximately $32 per kit. Currently, management is considering a production plan of 20,000 kits, though demand is uncertain. For the sake of simplicity, demand is assumed to be normally distributed with a mean of 18,000 units and a standard deviation of 1,500 units. In the event that management overestimates demand and is left with surplus inventory, the company will sell all remaining units at a discounted price. The discount price would start at $25 and would continue to decrease over time until all leftover kits are sold. As management is unsure how long it would take to deplete the remaining supply of leftover kits, the overall average discount price is therefore uncertain. Representatives from the sales department have compiled a distribution of possible average discount prices for leftover kits, which is displayed in the table below. Average Discount Price $25 $15 $10 Probability 0.60 0.25 0.15 Imagine you are creating a simulation model in R. Use the information provided above to answer the following questions. For questions dealing with object creation, ignore the 'assignment operator' portion of each command. a. You wish to create 10,000 simulations of the variable cost per kit, which you will store in an object called variable_cost. Which of the following commands correctly calculates these values? (Ignoring rounding) (1 pt) a. dunif(n=10000, min8, max=12) b. runif(n=10000, min=8, max=12) c. rnorm(n=10000, mean=8, sd=12) d. dnorm(x=10000, mean=8, sd=12) b. You wish to create 10,000 simulations of the quantity of kits demanded, which you will store in an object called demand. Which of the following commands correctly calculates these values? (1 pt) a. dnorm(n=10000, mean=18000, sd=1500) b. pnorm(n=10000, mean=18000, sd=1500) c. qnorm(n=10000, mean=18000, sd-1500) d. rnorm(p=10000, mean=18000, sd=1500) c. You have stored your planned supply value in an object called supply. Which of the following commands will correctly adjust the values of your demand object so that no simulated demand value exceeds the planned supply value of 20,000 kits? (1 pt) a. max(demand, supply) b. min(demand, supply) c. pmax (demand, supply) d. pmin(demand, supply) d. You wish to create 10,000 simulations of the average discount price of leftover kits, which you will store in an object called salvage_price. Which of the following commands correctly calculates these values? (1 pt) a. sample(x=C(25, 15, 10), size=10000, prob=c(0.60, 0.25, 0.15), replace=TRUE) b. sample(x=c(0.60, 0.25, 0.15), size=10000, prob=c(25, 15, 10), replace=TRUE) C. Sample(x=10000, size=c(25, 15, 10), prob=c(0.60, 0.25, 0.15), replace=FALSE) d. sample(x=C(25, 15, 10), size=c(0.60, 0.25, 0.15). prob=10000, replace=TRUE) e. Using your existing supply and demand objects, you wish to create an object called salvage_quantity that stores the total number of leftover kits in each simulation. Which of the following commands correctly calculates these values? (1 pt) a. ifelse(supply demand, supply, demand) c. ifelse(supply demand. (supply-demand), 0) f. You wish to create an object called profit that calculates the gross profit earned in each simulation (this model ignores fixed costs). Which of the following commands correctly calculates these values? (1 pt) a. (32"demand) + (salvage_price* salvage_quantity) - (variable_cost demand) b. (32* demand) + (salvage_price * salvage_quantity) - (variable_cost * supply) C. (32"supply) + (salvage_price* supply) - (variable_cost supply) d. (32" (supply-demand)) + (salvage_price" salvage_quantity) - (variable_cost) 8. Using your profit object, which of the following commands correctly calculates the probability that the company will actually realize a gross profit? (1 pt) a. mean(profit) b. sum(profit) / 10000 c. sum(profit>0) / 10000 d. profit / sum(profit) Ruby's Craft & Design is a U.S.-based mass producer of Halloween costumes (think pirates, nurses, zombies, and so on.) Each year, the company produces hundreds of thousands of pre-packaged "all in one costume kits, which are sold to Halloween supply stores around the country, who in turn sell the kits to consumers at a marked-up price. Each year, the company's managers tailor their production plan in an attempt to capitalize on any cultural trends that may result in increased demand for costumes reflecting specific characters or themes. In January 2021, a particularly amusing photograph of Vermont senator Bernie Sanders appearing to look quite unimpressed at the Presidential Inauguration ceremony became an overnight internet sensation and the subject of countless memes. Management immediately speculated that there might be a surge in demand for Bernie Sanders costumes for Halloween 2021. Through a hurried development process involving the company's design and production teams, management derived rough estimates for the costume kit's potential profit. Management estimates that the cost of materials and labor to produce each Bernie Sanders costume kit would be uniformly distributed between $8 and $12. The sales department estimates they could sell the kits to Halloween stores for approximately $32 per kit. Currently, management is considering a production plan of 20,000 kits, though demand is uncertain. For the sake of simplicity, demand is assumed to be normally distributed with a mean of 18,000 units and a standard deviation of 1,500 units. In the event that management overestimates demand and is left with surplus inventory, the company will sell all remaining units at a discounted price. The discount price would start at $25 and would continue to decrease over time until all leftover kits are sold. As management is unsure how long it would take to deplete the remaining supply of leftover kits, the overall average discount price is therefore uncertain. Representatives from the sales department have compiled a distribution of possible average discount prices for leftover kits, which is displayed in the table below. Average Discount Price $25 $15 $10 Probability 0.60 0.25 0.15 Imagine you are creating a simulation model in R. Use the information provided above to answer the following questions. For questions dealing with object creation, ignore the 'assignment operator' portion of each command. a. You wish to create 10,000 simulations of the variable cost per kit, which you will store in an object called variable_cost. Which of the following commands correctly calculates these values? (Ignoring rounding) (1 pt) a. dunif(n=10000, min8, max=12) b. runif(n=10000, min=8, max=12) c. rnorm(n=10000, mean=8, sd=12) d. dnorm(x=10000, mean=8, sd=12) b. You wish to create 10,000 simulations of the quantity of kits demanded, which you will store in an object called demand. Which of the following commands correctly calculates these values? (1 pt) a. dnorm(n=10000, mean=18000, sd=1500) b. pnorm(n=10000, mean=18000, sd=1500) c. qnorm(n=10000, mean=18000, sd-1500) d. rnorm(p=10000, mean=18000, sd=1500) c. You have stored your planned supply value in an object called supply. Which of the following commands will correctly adjust the values of your demand object so that no simulated demand value exceeds the planned supply value of 20,000 kits? (1 pt) a. max(demand, supply) b. min(demand, supply) c. pmax (demand, supply) d. pmin(demand, supply) d. You wish to create 10,000 simulations of the average discount price of leftover kits, which you will store in an object called salvage_price. Which of the following commands correctly calculates these values? (1 pt) a. sample(x=C(25, 15, 10), size=10000, prob=c(0.60, 0.25, 0.15), replace=TRUE) b. sample(x=c(0.60, 0.25, 0.15), size=10000, prob=c(25, 15, 10), replace=TRUE) C. Sample(x=10000, size=c(25, 15, 10), prob=c(0.60, 0.25, 0.15), replace=FALSE) d. sample(x=C(25, 15, 10), size=c(0.60, 0.25, 0.15). prob=10000, replace=TRUE) e. Using your existing supply and demand objects, you wish to create an object called salvage_quantity that stores the total number of leftover kits in each simulation. Which of the following commands correctly calculates these values? (1 pt) a. ifelse(supply demand, supply, demand) c. ifelse(supply demand. (supply-demand), 0) f. You wish to create an object called profit that calculates the gross profit earned in each simulation (this model ignores fixed costs). Which of the following commands correctly calculates these values? (1 pt) a. (32"demand) + (salvage_price* salvage_quantity) - (variable_cost demand) b. (32* demand) + (salvage_price * salvage_quantity) - (variable_cost * supply) C. (32"supply) + (salvage_price* supply) - (variable_cost supply) d. (32" (supply-demand)) + (salvage_price" salvage_quantity) - (variable_cost) 8. Using your profit object, which of the following commands correctly calculates the probability that the company will actually realize a gross profit? (1 pt) a. mean(profit) b. sum(profit) / 10000 c. sum(profit>0) / 10000 d. profit / sum(profit)