You know that the discrete metric only takes values of 1 and 0. Now suppose it comes from some norm ||.||. Then for any α in the underlying field of your vector space and x,y∈X, you must have that
∥α(x−y)∥=|α|∥x−y∥.
But now ||x−y|| is a fixed number and I can make α arbitrarily large and consequently the discrete metric does not come from any norm on X.
Step-by-step explanation:
hope this helps
Answer:
540
Step-by-step explanation:
90 is 16 2/3% of 540
Answer:
25 pesos 80 pw
pesos I'm not sure
Step-by-step explanation:
25. 80
Answer:
The probability is 1/2
Step-by-step explanation:
The time a person is given corresponds to a uniform distribution with values between 0 and 100. The mean of this distribution is 0+100/2 = 50 and the variance is (100-0)²/12 = 833.3.
When we take 100 players we are taking 100 independent samples from this same random variable. The mean sample, lets call it X, has equal mean but the variance is equal to the variance divided by the length of the sample, hence it is 833.3/100 = 8.333.
As a consecuence of the Central Limit Theorem, the mean sample (taken from independant identically distributed random variables) has distribution Normal with parameters μ = 50, σ= 8.333. We take the standarization of X, calling it W, whose distribution is Normal Standard, in other words

The values of the cummulative distribution of the Standard Normal distribution, lets denote it
, are tabulated and they can be found in the attached file, We want to know when X is above 50, we can solve that by using the standarization
