Grab your mirror and reflect your workspace, keyboard, left side, right side, and in front of your keyboard using your mirror.  Spend no more than 30 seconds here but make sure that the reflection is clear and hits the target areas.  Do NOT move anything else other than the mirror Do NOT leave the view of the camera or lean out of view Once done, place the mirror back down, click ‘Mirror reflection is complete’ and go to the NEXT question.  

 employee_id |    date    | job_title_id | salary ————-+————+————–+——–           1 | 2020-01-01 |            1 |  50000           1 | 2020-06-01 |            2 |  45000           2 | 2020-01-01 |            1 |  50000           3 | 2020-01-01 |            1 |  50000           4 | 2020-01-01 |            2 |  45000           5 | 2020-01-01 |            2 |  45000(6 rows) Assume that the two following transactions are happening concurrently on the above table called employee_salary in Postgres. What is the output of “SELECT date FROM employee_salary WHERE employee_id = 2;” in (7), if each statement is performed in the order in the parenthesis?   BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE; –(2)SELECT * FROM employee_salary; –(3)UPDATE  employee_salary SET date = ‘2020-03-01’ WHERE employee_id = 2; –(5)SELECT date FROM employee_salary WHERE employee_id = 2; –(7)COMMIT; –(8) BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE; ; –(1)UPDATE employee_salary SET date = ‘2020-02-01’ WHERE employee_id = 2; –(4)COMMIT; –(6)

CREATE TABLE employee_salary (     employee_id NUMERIC,    date DATE,    job_title_id NUMERIC,    salary NUMERIC,    PRIMARY KEY (employee_id, date)); employee_id |    date    | job_title_id | salary ————-+————+————–+——–           1 | 2020-01-01 |            1 |  50000           1 | 2020-06-01 |            2 |  45000           2 | 2020-01-01 |            1 |  50000           3 | 2020-01-01 |            1 |  50000           4 | 2020-01-01 |            2 |  45000           5 | 2020-01-01 |            2 |  45000(6 rows) For the above table definition, what is the output of the last query, “SELECT COUNT(*) FROM employee_salary WHERE employee_id = 2; ” in the following transaction? BEGIN;INSERT INTO employee_salary VALUES (2, ‘2020-01-01’, 2,45000);COMMIT;SELECT COUNT(*) FROM employee_salary WHERE employee_id = 2;

 employee_id |    date    | job_title_id | salary ————-+————+————–+——–           1 | 2020-01-01 |            1 |  50000           1 | 2020-06-01 |            2 |  45000           2 | 2020-01-01 |            1 |  50000           3 | 2020-01-01 |            1 |  50000           4 | 2020-01-01 |            2 |  45000           5 | 2020-01-01 |            2 |  45000(6 rows) Assume that the two following transactions are happening concurrently on the above table called employee_salary in Postgres. What is the output of “SELECT date FROM employee_salary WHERE employee_id = 2;” in (7), if each statement is performed in the order in the parenthesis?   BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE; –(2)SELECT * FROM employee_salary; –(3)UPDATE  employee_salary SET date = ‘2020-03-01’ WHERE employee_id = 2; –(5)SELECT date FROM employee_salary WHERE employee_id = 2; –(7)COMMIT; –(8) BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE; ; –(1)UPDATE employee_salary SET date = ‘2020-02-01’ WHERE employee_id = 2; –(4)COMMIT; –(6)

There are two tellers at a bank. Customers come into the bank randomly starting at 8am. As customers enter, they will wait in a queue if the tellers are busy (following a first-come first-serve discipline). The manager of the bank decides to the operations for 60 minutes one morning (8:00 – 9:00am) and recorded the events that happened during that time period.  Just Finished Event Variables Statistical Accumulators [EntityID,Time,Event] Q(t) B(t) Ʃ TS ∫Q(t) ∫B(t) [-, 0, Init] – – – – – [1, 0, Arr] 0 1 – – – [2, 8, Arr] 0 2 – – 8 [1, 15, Dep] 0 1 15 –  14 [3, 20, Arr] 0 2 – – 5 [4, 30, Arr] 1 2 – – 20 [2, 32, Dep] 0 2 24 2 [a5] [5, 42, Arr] 1 2 – – [a6] [6, 54, Arr] 2 2 – 12 [a7] [3, 55, Dep] 1 2 [a1]   [a3] [a8] [4, 56, Dep] 0 2 [a2]   [a4] [a9] [7, 60, Arr] 1 2 – – 8 [-, 60, End] 0 2 – – [a10]

What is an appropriate expression to use in Simio to model the interarrival time from 2-5pm?

Average number of people waiting in queue is considered a counter statistic.

A local vegan burger food truck needs help understanding different aspects of their operations. The food truck is open from 12-7pm daily. Customers arrive to the food truck according to Poisson process at a rate of 30 customers per hour from 12-2pm, 15 customers per hour from 2-5pm, and 32 customers per hour 5-7pm.  The food truck serves three types of burgers. The most popular burger takes a minimum of 3 minutes, a maximum of 7 minutes, and can usually be completed in 5 minutes. Customers place an order with the cashier then travel 6 meters to pick up their food once it is ready. The time is takes to place an order is uniformly distributed with a minimum of 1 minute and maximum of 3 minutes. There is one cashier and two food servers/cooks. Once customers receive their food, they depart. A Simio model was developed to model customer flow between 5-7pm. The model was run with 10 replications for 2 hours and a 5-hour warm-up period.  What is an appropriate expression to use in Simio to model the interarrival time from 12-2pm? Note: Assume a time units of minutes.

Now assume the inter-arrival time is uniformly distributed, what would be the inter-arrival time of the first 3 customers using LCM. Assume   as above. Inter-arrival time ~ Uniform(a,b) Uniform PDF:

Average number of people waiting in queue is considered a counter statistic.