The limitation of 5G mmWave, despite its high speed, is the fact that they have a short range.
- 5G simply means the fifth generation of wireless technology that has great speed and provides connectivity to cellphones.
- mmWave is the higher frequency radio band that is very fast. It should be noted that the 5G mmWave is super fast and is being used by large organizations to improve their work.
- The main limitation of 5G mmWave is that for one to use it, one has to be close to the 5G tower. This is why it's hard for people living in rural areas to benefit from it unless it's situated close to them.
- It should be noted that despite the fact 5G offers greater bandwidth, which is vital in relieving network congestion, there are still more improvements to be made in order for everyone to benefit.
In conclusion, the limitation of 5G mmWave, is that they have a short range.
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brainly.com/question/24664177
A. because everyone basically has seen it..
Answer: E. Never
geometric average return can NEVER exceed the arithmetic average return for a given set of returns
Explanation:
The arithmetic average return is always higher than the other average return measure called the geometric average return. The arithmetic return ignores the compounding effect and order of returns and it is misleading when the investment returns are volatile.
Arithmetic returns are the everyday calculation of the average. You take the series of returns (in this case, annual figures), add them up, and then divide the total by the number of returns in the series. Geometric returns (also called compound returns) involve slightly more complicated maths.
Answer:
- import math
-
- def standard_deviation(aList):
- sum = 0
- for x in aList:
- sum += x
-
- mean = sum / float(len(aList))
-
- sumDe = 0
-
- for x in aList:
- sumDe += (x - mean) * (x - mean)
-
- variance = sumDe / float(len(aList))
- SD = math.sqrt(variance)
-
- return SD
-
- print(standard_deviation([3,6, 7, 9, 12, 17]))
Explanation:
The solution code is written in Python 3.
Firstly, we need to import math module (Line 1).
Next, create a function standard_deviation that takes one input parameter, which is a list (Line 3). In the function, calculate the mean for the value in the input list (Line 4-8). Next, use the mean to calculate the variance (Line 10-15). Next, use sqrt method from math module to get the square root of variance and this will result in standard deviation (Line 16). At last, return the standard deviation (Line 18).
We can test the function using a sample list (Line 20) and we shall get 4.509249752822894
If we pass an empty list, a ZeroDivisionError exception will be raised.
