Answer:
(b). dependency and hedging.
Explanation:
In the management of risk, four common approaches for reducing risk are;
i. <em>Avoidance</em>: Especially if a risk involved in the management of a resource (or project) poses or presents a negative consequence, the best way to manage the risk simply avoid it by making sure it doesn't happen. This can be by cancelling a project or restructuring it.
ii. <em>Adaptation</em>: Another way of managing the risk associated with a resource (human or non-human resource) is to control the risk either by increasing resilience or reducing vulnerability. This is called adaptation.
iii. <em>Dependency: </em>This means accepting the risk since every project or business has inherently in it some risk associated. Dealing with it might be a way out especially knowing that there might be some experience to be gained in order to tackle similar situation in the future.
iv. <em>Hedging: </em>This means transferring the risk to some other business or organization. An example might be to get an insurance to manage this risk. In this case, the risk is transferred to the insurance company.
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Asyncronous is the answer I think
Answer:
Explanation:
The following is written in Python. It takes in a file, it then reads all of the elements in the file and adds them to a list called myList. Then it sorts the list and uses the elements in that list to calculate the median. Once the median is calculated it returns it to the user. The code has been tested and the output can be seen in the image below.
def find_median(file):
file = open(file, 'r')
mylist = []
for number in file:
mylist.append(int(number))
numOfElements = len(mylist)
mylist.sort()
print(mylist)
if numOfElements % 2 == 0:
m1 = numOfElements / 2
m2 = (numOfElements / 2) + 1
m1 = int(m1) - 1
m2 = int(m2) - 1
median = (mylist[m1] + mylist[m2]) / 2
else:
m = (numOfElements + 1) / 2
m = int(m) - 1
median = mylist[m]
return median
print("Median: " + str(find_median('file1.txt')))
