Given that the time to read data off a 7200 rpm disk drive will be roughly 75% of a 5400 rpm disk, at what idle time of the 7200 rpm disk will the power consumption be equal, on average, for the two disks?

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Answer 1
Answer:

Answer:

Given that the time to read data off a 7200 rpm disk drive will be roughly 75% of 5400 rpm disk, at 30% idle time of 7200 rpm disk will the power consumption be equal, on average for the two disk

Explanation:

Let the 7200-disk busy time = x  ( we need to calculate it)

As given in the question the in the given time to read data off a 7200 rpm disk drive will be roughly 75% of 5400 rpm disk. This mean that we equalize the equations of both disk power

7200-disk power      =     5400-disk power

here we apply the formula to calculate the 7200 rpm disk busy time

4.0 * (1-x) + 7.9 * x      =     2.9 * (1-x/0.75) + 7.0 * x/0.75

4.0 + 3.9 x                  =      2.9 – 3.87 x + 9.33 x

1.1                                =        5.47 x – 3.9 x

we calculate the value for x

Hence                   x = 0.70

Applying formula to calculate the idle time.

Idle time = 1.0 – 0.70 = 0.30


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Answer:

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Im going with true as well as he said

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A man-made world event that would affect the labor market would be a(n) _____.

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A group of statisticians at a local college has asked you to create a set of functions that compute the median and mode of a set of numbers, as defined in Section 5.4. Define these functions in a module named stats.py. Also include a function named mean, which computes the average of a set of numbers. Each function should expect a list of numbers as an argument and return a single number. Each function should return 0 if the list is empty. Include a main function that tests the three statistical functions. Ask users to enter the list of numbers, and then choose which function to apply to those numbers. After the single number is returned from the correct function, display to the user the list of numbers, the function selected and the answer in a format that is easy to understand.

Answers

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from functools import reduce

def mean(mylist):

   score = reduce(lambda x,y: x + y, mylist)/ len(mylist)

   return score

def median(mylist):

   sorted(mylist)

   list_len = len(mylist) % 2

   i = round(len(mylist)/2)

   x = len(mylist)//2

   if list_len == 0:

       median = (mylist[x] + mylist[x+1]) / 2  

   else:

       median = mylist[i]

   return median

def mode(mylist):

   unique = set(mylist)

   unique = list(unique)

   collector = [mylist.count(key) for key in unique]

   maxi = max(collector)

   loc = collector.index(maxi)

   return unique[loc]

def main():

   scores = input( 'Enter list of numbers: ').split(",")

   scores = [int(score) for score in scores]

   

   operation = input('Enter operation: ')

   operator = ['mean', 'median', 'mode']

   

   for x in iter(list, 0):

       if operation in operator:

           break

       print("Invalid operation: ")

       operation = input('Enter operation')

   

   index_loc = operator.index(operation)

   

   if index_loc == 0:

       return mean(scores)

   elif index_loc == 1:

       return median(scores)

       #return np.median(scores)  can be used of the defined function

   elif index_loc == 2:

       #return stats.mode(scores)[0]  can be used of the defined function

       return mode(scores)

print( main( ) )

Explanation:

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