Matlab Project -
In this project, we want you to gain practice in data manipulation by using existing UROS data and accepted industry standard tools (MATLAB and Excel) to show several descriptive analysis. You will be writing a MATLAB script that will analyze total costs, scores, and cost effectiveness of UROS groups from Fall 2016 and Fall 2017 and comparing them to your own competition results.
READ ALL DIRECTIONS.
1. Import data on Scores and Costs of groups from Fall 2016 and Fall 2017 semester groups from the following files (use "Data Manipulation" tutorial PPT if necessary):
a. You may use xlsread() or csvread().
b. ENES101_Project_Fall16_Costs.csv
c. ENES101_Project_Fall16_Scores.csv
i. This shows scores for two rounds. Only use the maximum score for each group in the following prompts.
d. ENES101_Project_Fall17_ScoresCosts.csv
e. Note: It is important that you do not edit the CSV files at all. ?
We will run your code using the original files and if you alter them your project will not run correctly when it is being graded.
f. See Matlab reference link at bottom of page for csvread() documentation
2. Compute cost effectiveness for each group in Fall 2017. CE = SCORE / COST. Display as an array/vector
3. Find the maximum, minimum, mean, mode, variance, and standard deviation of scores and costs for (1) Fall 2016, (2) Fall 2017, and (3) the combination of both semesters.
a. Clearly indicate and display your calculated values.
b. You may use Matlab's built-in functions.
4. Find the mean score? and total cost? (may use built in function) of each discussion of Fall 2017. (Store in two vectors for use in part 7a). It is not needed to display this as it will be used in part 7a.
5. Determine a meaningful threshold for the score outliers and count them for (1) Fall 2016, (2) Spring 2017, and (3) the combination of both semesters. Your output must include the threshold and the outlier count.
a. Write a comment for how you determined the meaningful threshold (doesn't need to be done in Matlab code, just include it).
6. Initialize variables for your team's final cost, best score, and EXPECTED cost effectiveness (you should have calculated this early in the design phase). Use these values to calculate the following:
i. Experimental error for cost effectiveness
ii. Use these values to place your own scores on the graphs described in #7
iii. Initialize these variables at the top of your script.
my_best_score = 3500;
my_expected_CE = 21.4; (and so on...)
7. You will be graphing the data after it is all gathered and calculated. Each graph should have a title, x and y-axis labels with units, and a legend if necessary.
a. Graph (scatter) the mean scores and costs of each Fall 2017 discussion found in #4.
Cost should be the dependent variable, score should be the independent variable.
On the same graph, mark the total mean with a "star" point (found in #3), and your own score with a "square" point.
b. Create a histogram of cost effectiveness values of the combined semesters. Use bin size = 9.
c. Plot the Least Squares Regression Line Cost Effective Value vs Score with score as the independent variable and cost effectiveness as the dependent variable. Use the data for the combined semesters. Mark your CE and Score with a "star" point. (Can use hold - see references)
Attachment:- Assignment Files.rar