Answer:
Step-by-step explanation:
The following code is written in Python. It is a function that takes in the number of miles as an argument and returns the number of laps that those miles represents.
def miles_to_laps(miles):
laps = miles / 0.25
return laps
Answer: The standard error for sample is 0.0686 .
Step-by-step explanation:
We know that the formula to find the standard error is given by :-
, where s = standard deviation
n= Sample size
As per given , we have
s= $0.4 and n= 34
Then , the standard error for sample is given by :-
Hence , the standard error for sample is 0.0686 .
A geometric mean is often used when comparing different items—finding a single "figure of merit" for these items—when each item has multiple properties that have different numeric ranges.[1]<span> For example, the geometric mean can give a meaningful "average" to compare two companies which are each rated at 0 to 5 for their environmental sustainability, and are rated at 0 to 100 for their financial viability. If an arithmetic mean were used instead of a geometric mean, the financial viability is given more weight because its numeric range is larger—so a small percentage change in the financial rating (e.g. going from 80 to 90) makes a much larger difference in the arithmetic mean than a large percentage change in environmental sustainability (e.g. going from 2 to 5). The use of a geometric mean "normalizes" the ranges being averaged, so that no range dominates the weighting, and a given percentage change in any of the properties has the same effect on the geometric mean. So, a 20% change in environmental sustainability from 4 to 4.8 has the same effect on the geometric mean as a 20% change in financial viability from 60 to 72.</span>
