<span>n/2 = average number of items to search.
Or more precisely (n+1)/2
I could just assert that the answer is n/2, but instead I'll prove it. Since each item has the same probability of being searched for, I'll simulate performing n searches on a list of n items and then calculate the average length of the searches. So I'll have 1 search with a length of 1, another search looks at 2, next search is 3, and so forth and so on until I have the nth search looking at n items. The total number of items looked at for those n searches will be:
1 + 2 + 3 + 4 + ... + n
Now if you want to find the sum of numbers from 1 to n, the formula turns out to be n(n+1)/2
And of course, the average will be that sum divided by n. So we have (n(n+1)/2)/n = (n+1)/2 = n/2 + 1/2
Most people will ignore that constant figure of 1/2 and simply say that if you're doing a linear search of an unsorted list, on average, you'll have to look at half of the list.</span>
Answer:
Explanation:
All of the above.
Companies will be attracted to nations that encourage market exchange and not restrict it, reward innovation, and protect people and property,
1. This is the hardest question to answer of all of them. It depends on who you read. The New York Times has a different policy than the Huffington Post. I'll say it is intended to be true.
2. True. That's why they are called specialty shops.
3. Sometimes. There are other possibilities. I think you are intended to say true.
4. True. They do.
5. False. It's the other way around.
Answer and Explanation:
The best type of investment income that is earned is tax-exempt that depend upon the commission only also the income that is spent should be bigger for the recipient
And at the time of seeking advice, the fee only should be likely to offer an unbiased advice because no other extra financial gains should be advised for an investment made except this professional fee
While the other options are ignored as they contain some interest regarding a commission for advising to their clients
I believe you have to search a URL of a website on the wayback machine search bar.
Then, you can browse the past-present years of how that website used to look like.
Hope this helps.
