If you could please provide the reasons for the Big O's i'd greatly appreciate it

You are a programmer for the Committee to Abolish CS 32, which wants to conduct a voter registration drive in West Los Angeles and the Valley. You have a collection of registered voters in those areas, and you have obtained a collection of licensed drivers from there. You want to find all drivers who are not already registered to vote. Both the driver and voter collections are randomly ordered. Here are two algorithms you are considering: for each licensed driver, { found = false for each voter, if the driver under consideration == the current voter, found = true break } if not found, print the drivers name } sort the collection of voters by name, using a good algorithm sort the collection of drivers by name, using a good algorithm start with the first voter and the first driver while not done with the voter collection and not done with the driver collection, { if the current driver's name is alphabetically the voter go on to the next voter } print the remaining names, if any, in the driver collection suppose the driver collection and the voter each contain about N names, where N is large. Of the following choices, what is the average case time complexity of the two algorithms? The choices are O(1), O(log N), O(N), O(N log N), O(N^2), O(N^2 log N), O(N^3) Using a particular computer, you run an algorithm with time complexity O(N^2) and find that it takes a little under 2 1/2 hours to run using the West LA and Valley data. Suppose you wish LA and the valley. (California has about 20 million drivers) About how long would that algorithm take on the California data, using the same computer