“Martial Arts R Us” (MARU) needs a database. MARU is a martial arts school with hundreds of students. The database must keep track of all the classes that are offered, who is assigned to teach each class, and which students attend each class. Also, it is important to track the progress of each student as they advance. Create a complete Crow’s Foot ERD for these requirements:Students are given a student number when they join the school. The number is stored along with their name, date of birth, and the date they joined the school.All instructors are also students, but clearly not all students are instructors. In addition to the normal student information, for all instructors, the date that they start working as an instructor must be recorded along with their instructor status (compensated or volunteer).An instructor may be assigned to teach any number of classes, but each class has one and only one assigned instructor. Some instructors, especially volunteer instructors, may not be assigned to any class.A class is offered for a specific level at a specific time, day of the week, and location. For example, one class taught on Mondays at 5:00 p.m. in Room 1 is an intermediate-level class. Another class taught on Mondays at 6:00 p.m. in Room 1 is a beginner-level class. A third class taught on Tuesdays at 5:00 p.m. in Room 2 is an advanced-level class.Students may attend any class of the appropriate level during each week, so there is no expectation that any particular student will attend any particular class session. Therefore, the attendance of students at each individual class meeting must be tracked.A student will attend many different class meetings, and each class meeting is normally attended by many students. Some class meetings may not be attended by any students. New students may not have attended any class meetings yet.At any given meeting of a class, instructors other than the assigned instructor may show up to help. Therefore, a given class meeting may have a head instructor and many assistant instructors, but it will always have at least the one instructor who is assigned to that class. For each class meeting, the date of the class and the instructors’ roles (head instructor or assistant instructor) need to be recorded. For example, Mr. Jones is assigned to teach the Monday, 5:00 p.m., intermediate class in Room 1. During a particular meeting of that class, Mr. Jones was the head instructor and Ms. Chen served as an assistant instructor.Each student holds a rank in the martial arts. The rank name, belt color, and rank requirements are stored. Most ranks have numerous rank requirements, but each requirement is associated with only one particular rank. All ranks except white belt have at least one requirement.A given rank may be held by many students. While it is customary to think of a student as having a single rank, it is necessary to track each student’s progress through the ranks. Therefore, every rank that a student attains is kept in the system. New students joining the school are automatically given the rank of white belt. The date that a student is awarded each rank should be kept in the system. All ranks have at least one student who has achieved that rank at some time.You can use either Microsoft Visio or Vertabelo to draw and export the ERD. To upload and submit your assignment, click the submission button below to find and select your saved document. Make sure that the file is saved with your last name in the file name. (Example: case2_Jones.pdf or case2_Jones.jpg)

Q.1) What it the Bog O of the following equation? 10n3 + 24n2 + 3n log n + 144 O(n^3) O(10n^3) O(n^2) O(n) Q.2) What it the Bog O of the following equation? n2 + n log n + 50 Group of answer choices O(n) O(n^2) O(log n^2) Q.3) What it the Bog O of the following equation? 5 log2n + 15 log n O(n) O(log n) O(1) Q.4) Exponential identity: (ab)n = an + bn True False Q.5) Match the following Big O notation with their respective operation. O(1) [ Choose ] Constant log n Logarithmic n log n quadratic Linear Cubic O(log n) [ Choose ] Constant log n Logarithmic n log n quadratic Linear Cubic O(n) [ Choose ] Constant log n Logarithmic n log n quadratic Linear Cubic O(n^2) [ Choose ] Constant log n Logarithmic n log n quadratic Linear Cubic Q.6) What is the Big-O of the code snippet below? #include <stdio.h> int main() { printf("Hello World"); } Group of answer choices O(Cn) O(n) O(1) O(n^2) Q.7) What is n in the pseudocode below? What is the Big-O? Pseudocode: list_Sum(A,n) { total =0 n = 8000 for i=0 to n-1 sum = sum + A[i] return sum } Group of answer choices n; O(1) 8000; O(A,i) 8000; O(n) 8000; O(n^2) Q.8 ) Exponential identity: aman = amn Group of answer choices True False Q.9) Logarithm identity: logb M + logb N = logb (M/N) Group of answer choices True False Q.10) Say your and your coworkers write an algorithm that takes in an array of numbers and returns the highest one, select the the three (3) best analysis below that describes your algorithm. Group of answer choices The absolute slowest it can run is Linear time – O(n) The absolute fastest it can run in Quadratic time – Ω(n^2) The absolute fastest it can run is Linear time – Ω(n) The absolute slowest it can run is constant time time – O(c) This algorithm be tightly asymptotically bound – so we can also say it’s Θ(n) Q.11) Match the analysis for Mileage problem: How much gas does it take to go 200 miles? Group of answer choices Straight, downhill, wind at your back [ Choose ] Best case No difference (200 miles is 200 Miles) Worst case Average case Can not predict “Average” terrain [ Choose ] Best case No difference (200 miles is 200 Miles) Worst case Average case Can not predict Winding, uphill, gravel road, inclement weather [ Choose ] Best case No difference (200 miles is 200 Miles) Worst case Average case Can not predict [ Choose ] Best case No difference (200 miles is 200 Miles) Worst case Average case Can not predict Q.12) Divide and Conquer Divide ==> solve the problem recursively. Conquer ==> put the problem into a number of sub-problems. Combine ==> put the problem into one solution to give a solution to the original problem. Group of answer choices True False Q.13) Heap Sort: Build the initial heap from the following array [10, 16, 54, 17, 19, 65, 31] Note: minimum 300 words. Q.14) Heap Sort - max heap: Build the initial heap from the following array [10, 16, 54, 17, 19, 65, 31] a. Show (draw) the max heap with the INITIAL max value a. Show (draw) the max heap AFTER the FIRST max value is removed b. Show (draw) the max heap AFTER the SECOND max value has been removed Note: minimum 300 words Q.15) what is the Worst case time complexity of quicksort? Group of answer choices O(n log n) O(n^2) O(n) Q.16) In Quick sort, the pivot can be any value within the array being sorted, commonly the value of the middle array element. Group of answer choices True False Q,17) MERGE SORT: Draw a diagram showing the merge sort of the following array. [5 2 4 7 1 3 2 6] Q.18) In heap sort - Max heap parents are larger/higher in value that the children In heap sort - Min heap children are larger/higher in value than the parent In quick sort - The parents are larger/higher in value that the children Group of answer choices True False Q.19) RESEARCH QUESTION: Solving the traveling salesman problem via brute-force search. 1. Explain the traveling salesman problem 2. Find an C++, or Python algorithm to solve the salesman problem (list information with an example) 3. What is the Big O? **Please pay attention to details. **Not the number of points Note: Minimum 500 words.

Lab 9 - Iterative EstimationIntroduction Function and aesthetics are often governed by mathematics. Beautiful examples of this can be found in nature, from the growth pattern of plants to the feature placement on the human face. Read a low-tech blog post (Links to an external site.) about how Aston Martin uses the golden ratio in car design. The Golden Ratio, phi, is defined by:φ=1+5√2φ=1+52 Equation 1. Exact PhiOne way that phi can be approximated is by taking the limit as n goes to infinity of a term in the Fibonacci sequence divided by it's proceeding term:φ=limn→∞fnfn−1φ=limn→∞fnfn−1 Equation 2. Estimated PhiBe sure to read the module page on Printing to Log Files because it contains code on how to compute terms of the Fibonacci sequence. Similarly, e can be approximated by:e=limn→∞(1+1n)ne=limn→∞(1+1n)n Equation 3. Estimated EWe will use our new input and output tools to create an iterative estimation program for both e and phi and compare our estimates to the true values.ProcedureAs usual, create a script, include a comment at the top explaining the purpose of the script, your name, and the date it was created.1. Use the menu function to ask the user if they would like to estimate e or phi. Use the input function to prompt the user to select a desired accuracy (tolerance) between .1 to .01. Include error checking to ensure the user input corresponds to your desired format. See the page on Input Error Checking for an example of how to do this. Use a conditional or switch statement to select between the two estimation functions that you will write (see below).2. To write your function to estimate e, create a function that accepts as input the user's desired tolerance and outputs the estimate of e and the number of iterations required to meet the desired accuracy. Your function header will look like: function [myE, numIters] = estE(tol). Use the limit equation above for large n and compare the result to MATLAB's built-in value for e. While the absolute value of the difference between e and your estimated e is greater than the tolerance, continue looping and increasing n.3. Use a similar method to estimate phi. Since MATLAB has no built-in constant for phi, create your own true phi using Equation 1 above.4. Create a text file to keep track of your progress at each iteration with the fprintf command (you may have to print every 100 iterations or even 1000 iterations if it takes a long time to reach the desired accuracy). Report the iteration, the estimated value and the absolute value of the difference between the estimated and true values at each step. Here's a sample output:...After 133 iterations, estimated e is 2.708133 and error is 0.010149.After 134 iterations, estimated e is 2.708208 and error is 0.010074.After 135 iterations, estimated e is 2.708282 and error is 0.010000.Turn-in InstructionsYou will need to submit .m files for each function you define, main.m, estE.m and estPhi.m as well as your text log file. Each .m file should include your name and function description in comments. Upload your files via Canvas. Let me know if you have any questions!