Question Solved1 Answer 2) The Eclipse Project folder must contain the following: 1. The following directory structure: - bin/ - Compiled Java files. - \( \mathrm{src} / \) - Java source code files: \( \mathrm{mru} / \) grade/application/ \( \mathrm{mru} / \) grade/controller/ \( \mathrm{mru} / \mathrm{grade} / \mathrm{model} / \) \( \mathrm{mru} / \mathrm{grade} / \mathrm{view} / 2) The Eclipse Project folder must contain the following: 1. The following directory structure: - bin/ - Compiled Java files. - \( \mathrm{src} / \) - Java source code files: \( \mathrm{mru} / \) grade/application/ \( \mathrm{mru} / \) grade/controller/ \( \mathrm{mru} / \mathrm{grade} / \mathrm{model} / \) \( \mathrm{mru} / \mathrm{grade} / \mathrm{view} / \) - \( \mathrm{doc} / \) - Generated Javadoc files. - Ensure the private option is checked and everything is included in the generated documentation. - lib/ - Any third-party libraries. This folder can be empty. - res/- Any resource or data files.Assignment Instructions 1. Use only Eclipse IDE. 2. The due date for this assignment is posted in \( \mathrm{D} 2 \mathrm{~L} \). The Problem In this assignment we are implementing a grade calculation system (explained later) along with some reports such as finding the top students with highest grade. The application will provide students with marks for three different courses. The program must have the following features: - When the program starts up, you must check to see if a file called StudentInfo.txt exists in the res directory. If it does exist, you must load the info from the file into an ArrayList, each record in the file is (should be) formatted as follows: Name,Phy,Chem, Math e.g.: Munima, \( 85,90,100 \) If the file does not exist, you must display an error message to the user. - After the start-up, you must present a menu that allows them to choose from the following options:Select one of the following options: (A) Display all Grades (S) Search Student (T) Top Students (E) Exit Enter your choice: - The options above must be selectable with the given letters. Both upper- and lower-case characters should be acceptable (This requirement applies to all of the menus in this assignment). - An invalid input should print an error message and re-prompt for an acceptable response (This also applies to all user inputs as well). (A) Display all Grades: This option prints the students name and the total marks with the grade. The rules for grades will be explained later(S) Search Student: This option asks for the student's name to find and will display the marks and the grade of the student. If the student's name is not in the database it will print an error message. Enter the name of the student: (T) Top Students: This option shows the information of the students with highest grades and returns to the main menu after pressing "Enter":To calculate the grade, you should first calculate the average marks for the student for the three courses provided in the data file. You display the average as the Total marks and depending on the average marks the grade will be calculated as follows:*@@version 1.0 */ Method with no parameters and no return type /** * <a description of what the method does \( \rangle \) */ Method with no parameters and has a return type \( / * * \) * 〈a description of what the method does * @return <a description of what is returned, including if errors are */Method with parameters and no return type \( / * * \) * 〈a description of what the method does> * */ Method with parameters and has return type /** * 〈a description of what the method does〉 * * @return */

The __str__ method of the Bank class (in bank.py) returns a string containing the accounts in random order. Design and implement a change that causes the accounts to be placed in the string in ascending order of name. Implement the __str__ method of the bank class so that it sorts the account values before printing them to the console. In order to sort the account values you will need to define the __eq__ and __lt__ methods in the SavingsAccount class (in savingsaccount.py). The __eq__ method should return True if the account names are equal during a comparison, False otherwise. The __it__ method should return True if the name of one account is less than the name of another, False otherwise. The program should output in the following format: Test for createBank: Name: Jack PIN: 1001 Balance: 653.0 Name: Mark PIN: 1000 Balance: 377.0 ... ... ... Name: Name7 PIN: 1006 Balance: 100.0

Perform the following operations: 10. Z = V(100)2 + (40)2 11. Z = V(4 x 10-8)2 + (50 x 10-92 12. Z = V(4E3)2 + (0.08E5)2 4 -1 13. 0 = sin 10 1 X 10-3 14. 0 = cos -1 50 X 10-4 4 15. X = V4² +6²

Find conditions on \( a, b, c \) so that \( v=(a, b, c) \) in \( \mathbf{R}^{3} \) belongs to \( W=\operatorname{span}\left(u_{1}, u_{2}, u_{3}\right) \). where \[ u_{1}=(1,2,0), u_{2}=(-1,1,2), u_{3}=(3,0,-4) \]

Perform the following operations: 10. Z = V(100)2 + (40)2 = 11. Z = V(4 x 10-82 + (50 x 10-22 = 12. Z = V(453)2 + (0.08E5)2 = 14. O = cos-11 X 10-3 50 X 10-4 - V4² +6² omplex Numbers Most scientific calculators provide a sequence of steps for performing required with complex numbers, but few are as direct as the T1-86 and 1 approach is so direct that it is Perform the following operations: 10. Z = V(100)2 + (40)2 = 11. Z = V(4 x 10-82 + (50 x 10-22 = 12. Z = V(453)2 + (0.08E5)2 = 14. O = cos-11 X 10-3 50 X 10-4 - V4² +6² omplex Numbers Most scientific calculators provide a sequence of steps for performing required with complex numbers, but few are as direct as the T1-86 and 1 approach is so direct that it is essentially a duplication of the basic operat

Note: I need help to implement this solution using R Studio Predicting Boston Housing Prices. The file BostonHousing.csv contains information collected by the US Bureau of the Census concerning housing in the area of Boston, Massachusetts. The dataset includes information on 506 census housing tracts in the Boston area. The goal is to predict the median house price in new tracts based on information such as crime rate, pollution, and number of rooms. The dataset contains 13 predictors, and the response is the median house price (MEDV). Table 6.9 describes each of the predictors and the response. a. Why should the data be partitioned into training and validation sets? What will the training set be used for? What will the validation set be used for? b. Fit a multiple linear regression model to the median house price (MEDV) as a function of CRIM, CHAS, and RM. Write the equation for predicting the median house price from the predictors in the model. c. Using the estimated regression model, what median house price is predicted for a tract in the Boston area that does not bound the Charles River, has a crime rate of 0.1, and where the average number of rooms per house is 6? What is the prediction error? d. Reduce the number of predictors: i. Which predictors are likely to be measuring the same thing among the 13 predictors? Discuss the relationships among INDUS, NOX, and TAX. ii. Compute the correlation table for the 12 numerical predictors and search for highly correlated pairs. These have potential redundancy and can cause multicollinearity. Choose which ones to remove based on this table. iii. Use stepwise regression with the three options (backward, forward, both) to reduce the remaining predictors as follows: Run stepwise on the training set. Choose the top model from each stepwise run. Then use each of these models separately to predict the validation set. Compare RMSE, MAPE, and mean error, as well as lift charts. Finally, describe the best model. PROBLEMS 169 PROBLEMS 6.1 Predicting Boston Housing Prices. The file Boston Housing.csv contains informa- tion collected by the US Bureau of the Census concerning housing in the area of Boston, Massachusetts. The dataset includes information on 506 census housing tracts in the Boston area. The goal is to predict the median house price in new tracts based on information such as crime rate, pollution, and number of rooms. The dataset con- tains 13 predictors, and the response is the median house price (MEDV). Table 6.9 describes each of the predictors and the response. TABLE 6.9 DESCRIPTION OF VARIABLES FOR BOSTON HOUSING EXAMPLE CRIM ZN INDUS CHAS NOX RM AGE DIS Per capita crime rate by town Proportion of residential land zoned for lots over 25,000 ft Proportion of nonretail business acres per town Charles River dummy variable (= 1 if tract bounds river; = 0 otherwise) Nitric oxide concentration (parts per 10 million) Average number of rooms per dwelling Proportion of owner-occupied units built prior to 1940 Weighted distances to five Boston employment centers Index of accessibility to radial highways Full-value property-tax rate per $10,000 Pupil/teacher ratio by town Percentage lower status of the population Median value of owner-occupied homes in $1000s RAD TAX PTRATIO LSTAT MEDV a. Why should the data be partitioned into training and validation sets? What will the training set be used for? What will the validation set be used for? b. Fit a multiple linear regression model to the median house price (MEDV) as a function of CRIM, CHAS, and RM. Write the equation for predicting the median house price from the predictors in the model. c. Using the estimated regression model, what median house price is predicted for a tract in the Boston area that does not bound the Charles River, has a crime rate of 0.1, and where the average number of rooms per house is 6? What is the prediction error? d. Reduce the number of predictors: i. Which predictors are likely to be measuring the same thing among the 13 predictors? Discuss the relationships among INDUS, NOX, and TAX. ii. Compute the correlation table for the 12 numerical predictors and search for highly correlated pairs. These have potential redundancy and can cause multi- collinearity. Choose which ones to remove based on this table. iii. Use stepwise regression with the three options (backward, forward, both) to reduce the remaining predictors as follows: Run stepwise on the training set. Choose the top model from each stepwise run. Then use each of these models separately to predict the validation set. Compare RMSE, MAPE, and mean error, as well as lift charts. Finally, describe the best model.

Perform the following operations: 10. Z = V(100)2 + (40) 11. Z = V(4 x 10-8)2 + (50 x 10-92 12. Z = V(453)2 + (0.08E5)2 4 13. 0 = sin 10 14. 0 = cos 1 X 10-3 50 X 10-4 4 15. X = V42 +6² y Numbers S Perform the following operations: 16. 62-120° to rectangular form 17.-5.8 + 19.6 to polar form 18. 6x 10-4 + j50 x 10-to polar form 19. 12 x 10-2102° to rectangular form 20. (6.8 mAZ-30°)(2.2 kN20°) = (polar form) 21. (6.8 k12 20°)(120 V 20°) 6.8 k22 - j3.6 k 2 (rectangular form) 22. 5230° + (6 - j6) - 2.22-90º = (polar form) 23. 6 + j8 (2Z80°)2 – (6 - j8) (rectangular form)