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Answer:Floating-point arithmetic is considered an esoteric subject by many people. This is rather surprising because floating-point is ubiquitous in computer systems. Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions such as overflow. This paper presents a tutorial on those aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating-point standard, and concludes with numerous examples of how computer builders can better support floating-point.
Explanation:
C
D is the old way.
A they have not done yet
B not even a thing
Answer:
- import statistics
- def st_dev(file_name):
- with open(file_name) as file:
- data = file.readlines()
- numList = []
- for x in data:
- numList.append(int(x))
-
- return statistics.pstdev(numList)
- print(st_dev("text1.txt"))
Explanation:
The solution code is written using Python.
To ease the calculation task, we can import Python statistics module to use the pstdev method to calculate the population standard deviation of a list of numbers (Line 1).
Next, create a st_dev function that take single argument file_name (Line 3). In the function, it will open the input file and read the data line by line (Line 4-5). Create a for loop to traverse through each line of the data which is an integer and append it to numList (Line 7-8). We can pass the numList to pstdev method (Line 10) and return the resulting standard deviation value as output.
We test the function by passing a file which hold a list of integer values in each line (Line 12).
8
9
12
11
21
15
16
10
7
13
And the output we shall get is 4.019950248448356
