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routes.py @application.route(‘/plotly_example’, methods=(‘GET’, ‘POST’))@login_requireddef plotly_example(): locations = classes.Location.query.all() output = plotly_map(locations) return render_template(‘plotly_map.html’, source=output) def plotly_map(locations): data = [ Scattermapbox( lat=[location.latitude for location in locations], lon=[location.longitude for location in locations], text=[location.name for location in locations], mode=’markers’, )] layout = Layout( autosize=True, hovermode=’closest’, mapbox=dict( accesstoken=mapbox_access_token, bearing=0, center=dict( lat=mean([location.latitude for location in locations]), lon=mean([location.longitude for location in locations]) ), pitch=100, zoom=10 ), ) fig = dict(data=data, layout=layout) output = plotly.offline.plot(fig, include_plotlyjs=False, output_type=’div’) return(output) plotly_map.html {% block content %} {{ (1) }} {% endblock %} Which filter should be used for (1) to render the json output on plotly_map.html?

Section F (Questions 19 ~ 24): A production line consists of two stations: one for production (Station A) and one for checking (Station B). Station A has job arrivals from outside, following a Poisson arrival process with rate 11/hr (a1 = 11/hr) and internal job arrivals from station B. A study shows that 50% of jobs produced at station A do not require checking but the other 50% of jobs are sent to the checking station (Station B). Station B gets an outside arrivals following a Poisson process with rate 5/hr (a2 = 5/hr). Among jobs at station B (both jobs from the outside and from station A), only 40% do not have defectives and leave the system. But the remaining 60% jobs need to be processed again at Station A and sent back to the end of the wait line of Station A. A defective item can visit stations A and B several times until it is fully fixed. A diagram of the production line is given below:    Station A has three servers and each processes one job at a time with a normally distributed service time with mean 6 minutes and variance 2 minutes2. Station B has one server and its processing time is also normal with mean 3 minutes and variance 0.25 minutes2.   

As the number of registered vehicles for the company is not large enough to accommodate all requests, the company re-directs 1/4 of requests to a third party (e.g., taxi) and only accepts 3/4 of the requests. Calculate the mean interarrival time between accepted requests in minutes. Show your work.

_____ subjects about the true purpose of a lab experiment following its conclusion is important for the ethical treatment of the subjects.

What is the hourly arrival rate to station A? 

Section E (Questions 15 ~ 18): A Ridepool driver has a car for a total capacity of 3 passengers and Ridepool customers send requests in a group of 1 or 2 passengers. Assume that this is the only Ridepool driver available in the area. To analyze the performance of her business, the driver models it as a CTMC with X(t) = (n1, n2) where n1 = the number of requests of a group size 1 that are accepted and currently being served and n2 =the number of requests of a group size 2 that are accepted and currently being served. Then its rate diagram is given below:    All rates in the diagram are hourly rates. The stationary distribution of the CTMC is given below: 

You are conducting a test marketing study in Tulsa, Oklahoma when your competitor introduces a 15% price cut in order to blunt the effects of your study.  A _____ effect has occurred during your test marketing study.

Now suppose that the arrival of Ride-X requests can be modeled as a non-homogeneous Poisson process. The arrival rate from 8 am to 5 pm is studied for many days. The following graph shows hourly arrival rates from 8 am to 5 pm.     We want to find the probability that the company receives less than 5 Ride-X requests from 8 am to 5 pm and the probability is 

Give the values of (i), (ii), and (iii) in the generator matrix.  (i) = [a]; (ii) = [b]; (iii) = [c]. 

Calculate the utilization of a server at station A.