C++

Math Revlew and Algorithm Analysls Use empirical analysis methods and code analysis methods to determine running time complexity in Big O notation. Reviow of Common Math Functions 1) Use Excel or some other graphing tool to graph the following equations. ymx y=2x y=2x y- log, x 2) Rank the graphs of the above equations by rate of growth, fastest (non-initial) growth first. 3) Match the shape of each graph with the closest common Big(O) curve and label them so. Empirical Analysis 4) Complete the table for each of the following functions. For each foo, write a small progranm with a loop where n is a counter from 0 to at least 64. Call the foo within the loop, passing it each value of n, and getting the return value from foo. Fill out a table with each n and its corres Capture your output and generate the tables. return value. You can skip some values of n when n starts to get biggish. int foo1 (int n) int counter e; for (int i 0; 1 <>