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A chemical firm produces sodium bisulfate in 100-pound bags. Demand for the product is 20 tons per day. The capacity for producing the product is 60 tons per day. Setup costs are $100, and storage and annual holding costs are $0.25 per bag. The firm operates 250 days per year. (Note: 1 ton = 2000 pounds). How many bags per run are optimal?  How many runs per year would there be? What is the average number of bags in inventory given the optimal lot size? Show all work.

All airplane passengers at the Lake City Regional airport must pass through security screening before proceeding to the boarding area. The airport has 8 screening stations available, and the facility manager must decide how many to have open at any particular time. The average service rate for processing passengers at a screening station is 50 passengers per minute. On Monday morning, the average inter-arrival time is 0.4 seconds per passenger. Assume exponential service times and Poisson arrival rates. The airport policy is to open the minimum number of screening stations that will ensure a 99% probability that the maximum line length is 5 passengers or fewer. How many screening stations should be opened? Show all work.

Which of the following must be completed each week for you to be counted present?

Which of the following is the best strategy for completing the readings assigned each week?

Background instructions for prepping to use MATLAB during exam: Here Here declare that you have done these things and you understand how to use the MATLAB.

Iterative Methods—non-Linear ————————————–

Write a script that includes (at bottom) any functions you write (if any) and that uses Linear Regression to  fit a line onto the given data fit a cubic (degree 3) polynomial onto the given data report results as discussed below plot results as discussed below Description of data:  the x variable is displacement.  The y variable is force for a “stiffening spring” that stiffens as it displaces.  It is not exactly linear in the force vs. displacement.  Let’s find out. Things to report.  Sum of squares of residuals (from mean) Sum of squares of residuals (from line fit) Sum of squares of residuals (from cubic fit) coefficient of determination (line) coefficient of determination (cubic) Things to plot Raw data, use ‘o’ Show y average, use ‘r’ Show the linear fit line, use ‘g’ Show the cubic fit line, use ‘b’ Data  (you should cut and paste this.) x data (independent) 56789101112131415161718192021222324252627282930 y data (dependent) 812.06967.661133.91305.71467.51650.91833.22002.62208.82369.92651.72772.12942.13380.93547.43842.93976.54238.94072.35017.75143.95551.45936.35855.76310.67343.5 Upload your script to the file upload below.              

Norms and Condition Numbers —————————————–

Linear Regression ——————————————-

Describe what the following code snippet is doing: fs = get_fs(curr); J = get_J(curr); next = curr + inv(J)*f