PF Touchstone 4: Analyzing Finances

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QUESTION

Touchstones are projects that illustrate your comprehension of the course material, help you refine skills, and demonstrate application of knowledge. You can work on a Touchstone anytime, but you can’t submit it until you have completed the unit’s Challenges. Once you’ve submitted a Touchstone, it will be graded and counted toward your final course score.

One way to check your progress towards your financial goals is to conduct a personal financial analysis. This will help you see how well you have stayed on track with your finances over time. Using technology can help you stay organized and help you visualize financial data, uncover trends, and effectively communicate your findings.

Your goal for this Touchstone is to build your confidence with technology. Using Microsoft Excel, you will organize three monthly budgets, visualize the data using graphs, and discuss the results. You’ll begin with the budget you created in Excel in the Unit 2 Touchstone. There are many types of technologies available for personal or professional use that can analyze and display personal financial data. Your experience with Excel spreadsheets in this course is a skill you can apply to other technology tools in your life and career. If you need a refresher on spreadsheets, review the tutorials in Unit 1, Challenge 3 of this course (Links are in Section D).

Touchstone 4: Analyzing Your Personal Finances

SCENARIO: Three months have passed since you created your first financial plan (i.e., the Unit 2 Touchstone). In that time, your budget has gone through some changes. The good news is that your income has increased because of your strong performance at work. However, health care and miscellaneous costs have gone up along with your earnings. You will need to reallocate your monthly budget based on these changes to see how you’re progressing toward your original savings goal.

While you appreciate numbers and figures, you also know that a strong financial analysis needs visual information. As part of your progress check, you’ve committed to creating a set of graphs that you can share with your financial advisor.

ASSIGNMENT: This assignment has two parts. In part 1, you will analyze personal finance data based on the scenario described above. You’ll use your problem solving and agility skills to balance three monthly budgets, and you’ll use your technology skill to graph the spending allocations in each of them. Finally, you’ll sharpen all three skills as you calculate and graph the progress you’re making toward your original savings goal.

In part 2, you will answer reflection questions about the decisions you made, identify how to create more savings opportunities, and make predictions about life and economic impacts that could affect the future of your plan.

For this assignment, you will:

Create three monthly budgets and perform a personal financial analysis using Microsoft Excel.

Summarize changes in expenditures between budgets.

Show the results of the analysis using appropriate graphs in Excel.

Explain how the graph types you have selected will help your financial advisor understand the data.

Discuss the results of the financial analysis including savings achievements, future budget modifications, and life impacts that could derail the budget in the coming year.

Reflect on what the analysis might reasonably look like in one year, accounting for economic factors such as inflation and the consumer price index.

Download the Excel template below, which further breaks down the steps involved in this assignment. You will return the completed template as your Touchstone submission.

  • Assignment Template
  • In order to foster learning and growth, all work you submit must be newly written specifically for this course. Any plagiarized or recycled work will result in a Plagiarism Detected alert. Review Touchstones: Academic Integrity Guidelines for more about plagiarism and the Plagiarism Detected alert. For guidance on the use of generative AI technology, review Ethical Standards and Appropriate Use of AI.
  • A. Assignment Guidelines
  • DIRECTIONS: Refer to the lists below throughout the assignment. Do not submit your Touchstone until it meets these guidelines.
  • 1. Analyzing Your Personal Finances
  • ? Have you populated the tables for Month 1, Month 2, and Month 3 with your budgeting information?? Have you verified that all amounts are displayed on a monthly (not annual) basis?? Have you verified that the sum of your expenditures (including savings) equals your employment income?? Have you populated the Savings Progress table?? Have you selected the most effective graph type for each of your data sets?? Have you left all predetermined formulas intact?

2. Reflection Questions

? Have you directly answered each question that was asked?? Have you provided sufficient evidence to support each of your answers?? Have you made clear and logical connections between your conclusions and the data used in the financial analysis?? Have you leveraged content from the course tutorials about economic factors?? Have you included sufficient detail in your answers?

B. Rubric

Advanced (100%)Proficient (85%)Acceptable (75%)Needs Improvement (50%)Non-Performance (0%)

Building Monthly Budgets (P1S1-P1S3) (20 points)

Create balanced budgets for Month 1, Month 2, and Month 3 using Excel’s formula feature and supplied data.All formulas are correct, income equals total expenditures, percentages are complete, and supplied data has been integrated. All values are realistic, and all units are displayed.Most formulas are correct, income equals total expenditures, percentages are mostly complete, and supplied data has been integrated. Most values are realistic, and all units are displayed.Some formulas are correct, income equals total expenditures, percentages are nearly complete, and supplied data has been integrated. Over half of the values are realistic, but not all units are displayed.Formulas were not used, income equals total expenditures, percentages are incomplete, and supplied data is missing. Only some values are realistic, but all units are displayed.Formulas were not used, income does not equal total expenditures, percentages are left blank, and supplied data is missing. Few values are realistic, and units are not displayed.

Creating Visuals (P1S4, P1S6) (20 points)

Graph expenditures for Month 1, Month 2, and Month 3, and graph the rate of savings over these three months.

All graphs are representative of the data tables and the axes are correctly labeled. The most effective graph type was chosen in all instances and cumulative savings shows an upward trend.Three to four graphs are representative of the data tables and the axes are correctly labeled. The most effective graph type was chosen in three to four instances and cumulative savings shows an upward trend.Two to three graphs are representative of the data tables and the axes are correctly labeled. The most effective graph type was chosen in two to three instances and cumulative savings shows an upward trend.One to two graphs are representative of the data tables, but the axes are not labeled. The most effective graph type was chosen in one to two instances and cumulative savings shows an upward trend.The graphs are not representative of the data tables and the axes are not labeled. The most effective graph type was chosen in only one instance and cumulative savings is trending incorrectly.

Measuring Savings Progress (P1Q3, P1Q4) (15 points)

Populate the Savings Progress table with savings per month, cumulative savings, and monthly shortages.

All the values for monthly savings match their respective data tables and monthly shortages are fully consistent with the original savings goal. Embedded formulas are present and accurate.All the values for monthly savings match their respective data tables and monthly shortages are mostly consistent with the original savings goal. Embedded formulas are present and mostly accurate.Two to three of the values for monthly savings match their respective data tables and monthly shortages are mostly consistent with the original savings goal. Embedded formulas are present and somewhat accurate.One to two of the values for monthly savings match their respective data tables but monthly shortages are not consistent with the original savings goal. Embedded formulas are present but inaccurate.Zero to one of the values for monthly savings matches its respective data table but monthly shortages are not consistent with the original savings goal. Embedded formulas are not present.

Choosing Graph Types (P2Q1) (15 points)

Give reasons for the graph types you selected for displaying expenditures and the savings rate. (150 words or less)

Rationale for choosing each graph type is thorough, evidence-based, and tightly connected to the tutorial content from the course.Rationale for choosing each graph type is mostly coherent, somewhat evidence-based, and largely connected to the tutorial content from the course.Rationale for choosing each graph type is somewhat coherent and evidence-based, but only loosely connected to the tutorial content from the course.Rationale for choosing each graph type is somewhat coherent and evidence-based, but no connections have been made to the tutorial content from the course.Rationale for choosing each graph type is incoherent, evidence is nonexistent, and no connections have been made to the tutorial content from the course.

Analyzing Results (P2Q2) 15 points)

Explain the results of your financial analysis in terms of progress, improvements, and anticipated derailments. (150 words or less)

QUESTION of savings progress is exceptional, improvement plans are logical and attainable, and derailments are closely tied to real-world economic factors.QUESTION of savings progress is well-written, improvement plans are logical and attainable, and derailments are largely tied to real-world economic factors. QUESTION of savings progress is acceptably written, improvement plans are logical and mostly attainable, and derailments are only loosely tied to real-world economic factors. QUESTION of savings progress is acceptably written, improvement plans are logical but not attainable, and derailments are only loosely tied to real-world economic factors.QUESTION of savings progress is poorly written, improvement plans are illogical, and derailments are not tied to real-world economic factors. 

Anticipating Economic Projections (P2Q3) (15 points)

Looking ahead one year, describe how economic factors might impact the financial analysis and how you might plan for them. (150 words or less)

The economic factors chosen are based on reality and tightly connected to the tutorial content from the course. The one-year plan is described clearly, is attainable, and is entirely logical based on the economic factors described.The economic factors chosen are based on reality and largely connected to the tutorial content from the course. The one-year plan is described clearly, is attainable, and is mostly logical based on the economic factors described.The economic factors chosen are mostly based on reality and sufficiently connected to the tutorial content from the course. The one-year plan is described sufficiently, is attainable, and is mostly logical based on the economic factors described.The economic factors chosen are loosely based on reality and somewhat connected to the tutorial content from the course. The one-year plan is described sufficiently but is not attainable or logical, based on the economic factors described.The economic factors chosen are not based on reality and are not connected to the tutorial content from the course. The one-year plan is described poorly, is not attainable, and is illogical based on the economic factors described.

IUPUI CSCI448Color-based image segmentation clustering

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Question

Perform k-means color clustering in two photos you will take, and detect skin regions. These two images should depict your hand on different backgrounds.

The scopes of this assignment: (1) to identify qualitatively/visually, what data (images) can make processing easier, including resolution, distance from target, background complexity, lighting, etc.; (2) to evaluate the computational challenges; (3) to study a simple clustering method in image region segmentation and be able to identify alternative solutions.  

Specifically:

0. take two photos of your hand in a setting you define (lighting, background, distance from camera), each with a different background.
1. implement “repetitive” k-means, as described below; use k = {2, 3, 5} (number of clusters).
2. cluster the image pixels with respect to color {R,G,B}, and display the corresponding k probability maps (for each of the three k-values).
3. threshold the probabilities to detect skin regions (binary outcome for each pixel): each pixel will have k (for a given k-value) probability values; for a pixel to be considered as skin region, the highest among its k probability values should correspond to the skin color cluster(s) and should be over a threshold.

Repetitive k-means:
Every execution of k-means is an iterative process, from start to convergence, using k randomly chosen cluster centroids during initialization: pick k random pixels, and use their {R,G,B}-values. Consider an execution (from initialization to convergence) as a single instance in a “bigger” repetitive process, where you will execute k-means several times, each with different randomly chosen centroids/pixel {R,G,B} values.  The output should be probability maps.

For example, for k=3, and 100 repetitions, the output should be three maps:
P(x belongs to cluster i) | {Rx,Gx,Bx} ), for i={1,2,3} ({Rx,Gx,Bx} are the chromatic values of x). That is, calculate P(x belongs to cluster 1) | {Rx,Gx,Bx} ), P(x belongs to cluster 2) | {Rx,Gx,Bx} ), and P(x belongs to cluster 3) | {Rx,Gx,Bx} ). After the 100 executions of k-means, each with different initialization, you will observe each pixel (the corresponding {R,G,B}) how many times it is assigned to cluster 1, to cluster 2, and to cluster 3. For each pixel, convert the frequency of assignment (to cluster 1, 2, and 3) into percentage: e.g., a% found in cluster 1, b% found in cluster 2, c% found in cluster 3, with a+b+c = 100.

Example: routine M = my_rep_kmeans(X, k)

step 0: Define k value

step 1: load image into variable X (of size h x w x 3) since it is a color image (you may have to convert the {R,G,B} values into double precision if they are in a different format, e.g., “uint8” in Matlab)

step 1.5: numofexec = 1 (counts how many times you execute k-means (see below); initialize to 1)

step 2: randomly choose k pixels and use their color as initialization in the color space; i.e., {C1, …, Ck}, where C1, …, Ck are 3×1 (or 1×3) matrices {R,G,B}-values. The random choice of k pixels can be based on cartesian coordinates: for each initial centroid, use the {R,G,B}-value of a randomly chosen pixel X(i,j) with coordinates i in [1,h] and j in [1,w].

step 3: perform k-means: Xout = somekmeansfun(X, k, [C1, …, Ck]) ==> notice the input: variable k, and random centroids. The output Xout should be of size h x w x 1. For more compact representation (see below), M( : , : , numofexec) = Xout.  See notes below.

step 3.5: align cluster numbers for consistency throughout all repetitions based on a fixed rule, e.g., sort clusters based on the {R,G,B}-values of their centroids (say, cluster 1 is the cluster with the lowest R-value, etc.)

step 4: numofexec = numofexec + 1; Repeat step 2 ; repeat step 3, repeat step 3.5. 

step 5: At the end, you will have a 3-dimensional matrix M: size(M) = [h, w, numofexec].  You can visualize the cluster assignments of each pixel X(i, j) as: v = M(i, j, 🙂 ==> size(v) = [1, numofexec]   (1 x numofexec) ==> plot it. For each pixel, you can calculate the frequency of its assignment to each cluster using v.

step 5: Calculate P1 = P( all hxw pixels belong to cluster 1), P2 = P( all hxw pixels belong to cluster 2), …, Pk = P( all hxw pixels belong to cluster k). Note that P1, P2, …, Pk are 2D matrices (hxw).

step 6: Display as color/heat maps the 2D matrices/probability maps P1, P2, …, Pk.

The whole procedure should be done three times, for the three different values of k =2, 3, 5.

NUU Web and Graphics Design 3x3x3 Rubiks Cube Javascript Questions

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Question

I am trying to create a 3x3x3 Rubik’s cube using HTML and JavaScript. My current issue is that the current code uses 27 cubies to create the overall cube. The issue is the cubies currently have the 6 colours around each cubie. Whereas, I want each face of the overall cube to have 6 colours. So the corner cubies would have 3 colours, the middle edge cubies would have 2 colours, and the center cubies of each face would have 1 colour. The first image shows you what the current code looks like and the second image will show a rough idea of what I want it to look like. I also want to be able to rotate the faces using keys. For example: The left face could be rotated should rotate anti-clockwise using ‘q’ and clockwise with ‘a’ the right face should rotate anti-clockwise using ‘w’ and clockwise using ‘s’. The front face should rotate anti-clockwise using ‘e’ and clockwise using ‘d’ and the back face should rotate anti-clockwise using ‘r’ and clockwise using ‘f’. The top face should rotate anti-clockwise using ‘u’ and clockwise using ‘i’ and the bottom face to rotate anti-clockwise using ‘j’ and clockwise using ‘k’. This is the current code that correlates to the first image (The rotations are incorrect in this code):

<!DOCTYPE html>

<html lang=”en”>

<head>

<meta charset=”UTF-8″>

<meta name=”viewport” content=”width=device-width, initial-scale=1.0″>

<title>3x3x3 Rubik’s Cube with Three.js</title>

<style>

body {

margin: 0;

overflow: hidden;

}

#rubiksCubeContainer {

width: 100vw;

height: 100vh;

}

</style>

</head>

<body tabindex=”0″>

<div id=”rubiksCubeContainer”></div>

<script type=”module”>

import * as THREE from ‘https://cdn.jsdelivr.net/npm/three@0.121.1/build/three.module.js’;

const scene = new THREE.Scene();

const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 100);

const renderer = new THREE.WebGLRenderer();

renderer.setSize(window.innerWidth, window.innerHeight);

document.getElementById(‘rubiksCubeContainer’).appendChild(renderer.domElement);

const cubieGeometry = new THREE.BoxGeometry(1, 1, 1);

const createCubie = (x, y, z) => {

const colors = [0xff0000, 0x00ff00, 0x0000ff, 0xffff00, 0xff8800, 0xffffff];

const materials = colors.map(color => new THREE.MeshBasicMaterial({ color }));

const cubie = new THREE.Mesh(cubieGeometry, materials);

cubie.geometry.faces.forEach((face, index) => {

face.materialIndex = Math.floor(index / 2); // Each face has two triangles

});

cubie.position.set(x, y, z);

scene.add(cubie);

return cubie;

};

const createLayer = (layerNumber) => {

const group = new THREE.Group();

for (let x = 0; x < 3; x++) {

for (let y = 0; y < 1; y++) {

for (let z = 0; z < 3; z++) {

const cubie = createCubie(x * 1.1 – 1.1, y * 1.1 – 1.1, z * 1.1 – 1.1);

group.add(cubie);

}

}

}

group.position.set(0, 1.1 * layerNumber, 0);

scene.add(group);

return group;

};

const topLayerGroup = createLayer(1);

const middleLayerGroup = createLayer(0);

const bottomLayerGroup = createLayer(-1);

camera.position.set(5, 5, 8);

camera.lookAt(scene.position);

const animate = () => {

requestAnimationFrame(animate);

renderer.render(scene, camera);

};

window.addEventListener(‘resize’, () => {

camera.aspect = window.innerWidth / window.innerHeight;

camera.updateProjectionMatrix();

renderer.setSize(window.innerWidth, window.innerHeight);

});

window.addEventListener(‘keydown’, (event) => {

switch (event.key) {

case ‘i’:

rotateGroupCounterClockwise(topLayerGroup);

break;

case ‘u’:

rotateGroupClockwise(topLayerGroup);

break;

case ‘k’:

rotateGroupCounterClockwise(middleLayerGroup);

break;

case ‘j’:

rotateGroupClockwise(middleLayerGroup);

break;

case ‘m’:

rotateGroupCounterClockwise(bottomLayerGroup);

break;

case ‘n’:

rotateGroupClockwise(bottomLayerGroup);

break;

case ‘a’:

rotateColumnCounterClockwise(0);

break;

case ‘q’:

rotateColumnClockwise(0);

break;

case ‘s’:

rotateColumnCounterClockwise(1);

break;

case ‘w’:

rotateColumnClockwise(1);

break;

case ‘d’:

rotateColumnCounterClockwise(2);

break;

case ‘e’:

rotateColumnClockwise(2);

break;

}

});

animate();

const rotateGroupCounterClockwise = (group) => {

group.rotateOnAxis(new THREE.Vector3(0, 1, 0), Math.PI / 2);

updateRenderer();

};

const rotateGroupClockwise = (group) => {

group.rotateOnAxis(new THREE.Vector3(0, 1, 0), -Math.PI / 2);

updateRenderer();

};

const rotateColumnCounterClockwise = (columnIndex) => {

const columnCubies = [

// List of cubies in the specified column

topLayerGroup.children[columnIndex * 3],

middleLayerGroup.children[columnIndex * 3],

bottomLayerGroup.children[columnIndex * 3],

topLayerGroup.children[columnIndex * 3 + 1],

middleLayerGroup.children[columnIndex * 3 + 1],

bottomLayerGroup.children[columnIndex * 3 + 1],

topLayerGroup.children[columnIndex * 3 + 2],

middleLayerGroup.children[columnIndex * 3 + 2],

bottomLayerGroup.children[columnIndex * 3 + 2],

];

// Use the X-axis as the rotation axis (1, 0, 0)

const axis = new THREE.Vector3(1, 0, 0);

// Rotate each cubie in the column on the specified axis

columnCubies.forEach((cubie) => cubie.rotateOnAxis(axis, Math.PI / 2));

// Update the renderer to reflect the changes

updateRenderer();

};

// For rotating the column clockwise

const rotateColumnClockwise = (columnIndex) => {

const columnCubies = [

// List of cubies in the specified column

topLayerGroup.children[columnIndex * 3],

middleLayerGroup.children[columnIndex * 3],

bottomLayerGroup.children[columnIndex * 3],

topLayerGroup.children[columnIndex * 3 + 1],

middleLayerGroup.children[columnIndex * 3 + 1],

bottomLayerGroup.children[columnIndex * 3 + 1],

topLayerGroup.children[columnIndex * 3 + 2],

middleLayerGroup.children[columnIndex * 3 + 2],

bottomLayerGroup.children[columnIndex * 3 + 2],

];

// Use the X-axis as the rotation axis (1, 0, 0)

const axis = new THREE.Vector3(1, 0, 0);

// Rotate each cubie in the column on the specified axis

columnCubies.forEach((cubie) => cubie.rotateOnAxis(axis, -Math.PI / 2));

// Update the renderer to reflect the changes

updateRenderer();

updateRenderer();

};

const updateRenderer = () => {

renderer.render(scene, camera);

};

</script>

</body>

</html>

MySQL Question

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Question

# Insert into University

use university;
select * from course;

INSERT INTO `university`.`course` (`CourseDept`, `CourseNumber`, `CourseDesc`, `CourseUnits`)
VALUES
(‘FIN’, ‘760’, ‘Derivatives’, ‘3’),
(‘DAT’, ‘480’, ‘Visualization’, ‘3’),
(‘ART’, ‘560’,’Renaissance’,’2′),
(‘ENG’, ‘240’,’Short Stories’,’3′),
(‘IS’, ‘120’,’Database Fundamentals’,’1′),
(‘IS’, ‘420’,’Distributed Databases’,’4′),
(‘IS’, ‘650’,’Cybersecurity’,’4′),
(‘FIN’, ‘700’,’Bond Valuation’,’4′),
(‘DAT’, ‘460’,’Cryptocurrency’,’3′),
(‘ART’, ‘250’,’Graphic Design’,’3′),
(‘DAT’, ‘515’,’Data Mining’,’4′);

# Faculty

INSERT INTO `university`.`faculty` (`FacFirstName`, `FacLastName`, `FacRank`, `FacStartDate`) VALUES
(‘Victoria’,’Emmerline’,’Prof’,’1994-06-01′),
(‘Greg’,’Brown’,’Prof’,’2010-09-01′),
(‘Ed’,’Smith’,’Prof’,’1998-01-01′),
(‘Tracey’,’Winter’,’Prof’,’2002-03-01′),
(‘Taylor’,’Languid’,’Asst’,’2010-01-01′),
(‘Kimberly’,’Smathers’,’Asst’,’2011-03-01′),
(‘Lee’,’Marshall’,’Asst’,’2009-06-01′),
(‘Rajean’,’Jackson’,’Asst’,’2014-09-01′),
(‘Lee’,’Sepulveda’,’Assc’,’2018-01-01′),
(‘Sylvester’,’Jackson’,’Assc’,’2015-09-01′),
(‘Laura’,’Smith’,’Assc’,’2015-03-01′),
(‘Colin’,’Ensenada’,’Assc’,’2015-01-01′),
(‘Jackson’,’Nelson’,’Assc’,’2017-03-01′),
(‘Victoria’,’Emmerline’,’Prof’,’2019-06-01′),
(‘Victoria’,’Nelson’,’Prof’,’2019-06-01′),
(‘Jackson’,’Smith’,’Prof’,’2021-02-01′),
(‘Jackson’,’Emmerline’,’Prof’,’2021-09-01′)
;

select * from faculty;

# Insert students
INSERT INTO `university`.`student` (`stdFirstName`, `stdLastName`, `stdMajor`, `stdStanding`, `stdResidence`)
VALUES
(‘Jordan’,’Adams’,’FIN’,’SO’,’Out of state’),
(‘Hailey’,’Alexander’,’IS’,’JR’,’In state’),
(‘Iris’,’Allen’,’DATA’,’SR’,’In state’),
(‘Leo’,’Allen’,’FIN’,’SR’,’In state’),
(‘Cameron’,’Anderson’,’DATA’,’FR’,’Out of state’),
(‘Wyatt’,’Anderson’,’FIN’,’JR’,’In state’),
(‘Zion’,’Bailey’,’FIN’,’FR’,’In state’),
(‘Joseph’,’Baker’,’FIN’,’FR’,’In state’),
(‘Abigail’,’Barnes’,’LSA’,’SR’,’Out of state’),
(‘William’,’Bell’,’IS’,’SR’,’Out of state’),
(‘Adalyn’,’Bennett’,’FIN’,’JR’,’In state’),
(‘Lily’,’Brooks’,’DATA’,’SO’,’Out of state’),
(‘Angel’,’Brown’,’IS’,’JR’,’In state’),
(‘Mateo’,’Brown’,’IS’,’SR’,’In state’),
(‘Kinsley’,’Bryant’,’LSA’,’JR’,’In state’),
(‘Hannah’,’Butler’,’IS’,’JR’,’In state’),
(‘Madison’,’Campbell’,’IS’,’SR’,’In state’),
(‘Justin’,’Carter’,’FIN’,’SR’,’In state’),
(‘Gabriel’,’Clark’,’DATA’,’SR’,’Out of state’),
(‘Josiah’,’Clark’,’DATA’,’FR’,’In state’),
(‘Penelope’,’Cole’,’LSA’,’FR’,’In state’),
(‘Avery’,’Coleman’,’IS’,’SR’,’In state’),
(‘Michael’,’Collins’,’IS’,’FR’,’In state’),
(‘Trinity’,’Cook’,’FIN’,’SO’,’In state’),
(‘Emma’,’Cooper’,’FIN’,’SO’,’In state’),
(‘Isabella’,’Cox’,’FIN’,’SR’,’In state’),
(‘Michael’,’Cruz’,’DATA’,’SO’,’In state’),
(‘Aniyah’,’Davis’,’DATA’,’SO’,’In state’),
(‘Caleb’,’Davis’,’DATA’,’SO’,’In state’),
(‘William’,’Bell’,’UNK’,’FR’,’In state’),
(‘Matthew’,’Edwards’,’DATA’,’SR’,’In state’),
(‘Ethan’,’Ellis’,’FIN’,’JR’,’Out of state’),
(‘Malik’,’Evans’,’FIN’,’SO’,’In state’),
(‘Oliver’,’Fisher’,’FIN’,’SO’,’In state’),
(‘Arianna’,’Flores’,’LSA’,’JR’,’In state’),
(‘Anna’,’Ford’,’DATA’,’FR’,’In state’),
(‘Ellie’,’Foster’,’LSA’,’SR’,’In state’),
(‘James’,’Freeman’,’IS’,’SO’,’Out of state’),
(‘Diamond’,’Garcia’,’UNK’,’FR’,’In state’),
(‘Levi’,’Garcia’,’FIN’,’SO’,’In state’),
(‘Muhammad’,’Gibson’,’DATA’,’SR’,’In state’),
(‘Jacob’,’Gomez’,’FIN’,’SO’,’Out of state’),
(‘Kaylee’,’Gonzales’,’FIN’,’FR’,’In state’),
(‘Joshua’,’Gonzalez’,’LSA’,’JR’,’In state’),
(‘Brooklyn’,’Graham’,’FIN’,’SO’,’In state’),
(‘Amelia’,’Gray’,’IS’,’JR’,’In state’),
(‘Jordan’,’Green’,’IS’,’JR’,’In state’),
(‘Paisley’,’Griffin’,’FIN’,’SO’,’In state’),
(‘Isaiah’,’Hall’,’LSA’,’FR’,’In state’),
(‘Nathan’,’Hall’,’LSA’,’SO’,’Out of state’),
(‘Sarah’,’Hamilton’,’IS’,’SR’,’In state’),
(‘Daniel’,’Harris’,’DATA’,’JR’,’In state’),
(‘Isaiah’,’Harris’,’DATA’,’SR’,’In state’),
(‘Jayden’,’Harrison’,’LSA’,’FR’,’In state’),
(‘Addison’,’Hayes’,’DATA’,’FR’,’In state’),
(‘Aaliyah’,’Henderson’,’LSA’,’SO’,’Out of state’),
(‘James’,’Hernandez’,’LSA’,’FR’,’In state’),
(‘Jaylen’,’Hill’,’LSA’,’SR’,’In state’),
(‘Mia’,’Howard’,’LSA’,’SO’,’In state’),
(‘Camilla’,’Hughes’,’LSA’,’SR’,’In state’),
(‘Christian’,’Jackson’,’FIN’,’SR’,’Out of state’),
(‘Connor’,’Jackson’,’FIN’,’FR’,’In state’),
(‘Charlotte’,’James’,’FIN’,’FR’,’In state’),
(‘Ella’,’Jenkins’,’DATA’,’SO’,’In state’),
(‘Alexandra’,’Johnson’,’IS’,’JR’,’Out of state’),
(‘Matthew’,’Johnson’,’FIN’,’SR’,’In state’),
(‘Alyssa’,’Jones’,’LSA’,’SO’,’In state’),
(‘Jayce’,’Jones’,’FIN’,’FR’,’In state’),
(‘Mason’,’Jordan’,’FIN’,’JR’,’Out of state’),
(‘Chloe’,’Kelly’,’DATA’,’SR’,’In state’),
(‘Jasmine’,’King’,’LSA’,’SR’,’Out of state’),
(‘Hannah’,’Lee’,’FIN’,’JR’,’In state’),
(‘Samuel’,’Lee’,’IS’,’SR’,’Out of state’),
(‘Dylan’,’Lewis’,’FIN’,’SO’,’In state’),
(‘Hailey’,’Lewis’,’IS’,’SR’,’In state’),
(‘Mila’,’Long’,’LSA’,’SO’,’In state’),
(‘Jayla’,’Lopez’,’FIN’,’FR’,’In state’),
(‘Sebastian’,’Marshall’,’IS’,’SO’,’In state’),
(‘David’,’Martin’,’UNK’,’FR’,’In state’),
(‘Isaac’,’Martin’,’DATA’,’JR’,’In state’),
(‘Cameron’,’Martinez’,’LSA’,’JR’,’In state’),
(‘Elijah’,’Martinez’,’IS’,’FR’,’In state’),
(‘Carter’,’Mcdonald’,’LSA’,’FR’,’In state’),
(‘Anthony’,’Miller’,’FIN’,’JR’,’In state’),
(‘Luke’,’Miller’,’LSA’,’SO’,’In state’),
(‘Kayla’,’Mitchell’,’IS’,’FR’,’In state’),
(‘Brianna’,’Moore’,’IS’,’JR’,’Out of state’),
(‘Jack’,’Moore’,’UNK’,’FR’,’In state’),
(‘Tyler’,’Morgan’,’FIN’,’SR’,’In state’),
(‘Sydney’,’Morris’,’LSA’,’SR’,’In state’),
(‘Xavier’,’Murphy’,’IS’,’FR’,’In state’),
(‘Benjamin’,’Murray’,’FIN’,’SR’,’Out of state’),
(‘Isabelle’,’Myers’,’IS’,’SO’,’In state’),
(‘Josiah’,’Nelson’,’DATA’,’SR’,’In state’),
(‘Alexander’,’Ortiz’,’DATA’,’SR’,’In state’),
(‘Elijah’,’Owens’,’FIN’,’SR’,’In state’),
(‘Makayla’,’Parker’,’LSA’,’JR’,’In state’),
(‘Nora’,’Patterson’,’IS’,’FR’,’Out of state’),
(‘Kennedy’,’Perez’,’LSA’,’FR’,’In state’),
(‘Scarlett’,’Perry’,’LSA’,’FR’,’In state’),
(‘Zoe’,’Peterson’,’LSA’,’SR’,’In state’),
(‘Laila’,’Phillips’,’DATA’,’SO’,’In state’),
(‘Maya’,’Powell’,’IS’,’SR’,’Out of state’),
(‘Evelyn’,’Price’,’DATA’,’SO’,’Out of state’),
(‘Layla’,’Ramirez’,’LSA’,’SO’,’In state’),
(‘Tiana’,’Reed’,’LSA’,’JR’,’In state’),
(‘Logan’,’Reynolds’,’LSA’,’JR’,’In state’),
(‘Ava’,’Richardson’,’DATA’,’SO’,’In state’),
(‘Olivia’,’Rivera’,’DATA’,’SR’,’In state’),
(‘Kevin’,’Roberts’,’UNK’,’FR’,’In state’),
(‘Ethan’,’Robinson’,’LSA’,’SR’,’Out of state’),
(‘Nicholas’,’Robinson’,’LSA’,’JR’,’In state’),
(‘Gabrielle’,’Rodriguez’,’DATA’,’SO’,’Out of state’),
(‘Lincoln’,’Rodriguez’,’DATA’,’JR’,’In state’),
(‘Taylor’,’Rogers’,’IS’,’SO’,’In state’),
(‘Madison’,’Ross’,’UNK’,’FR’,’In state’),
(‘Madelyn’,’Russell’,’IS’,’SR’,’In state’),
(‘Neve’,’Sanchez’,’LSA’,’SR’,’In state’),
(‘Harper’,’Sanders’,’LSA’,’SR’,’In state’),
(‘Jeremiah’,’Scott’,’IS’,’SR’,’In state’),
(‘Leah’,’Simmons’,’LSA’,’FR’,’Out of state’),
(‘Aliyah’,’Smith’,’LSA’,’SR’,’In state’),
(‘Ryan’,’Smith’,’DATA’,’FR’,’In state’),
(‘Nathan’,’Stewart’,’LSA’,’JR’,’Out of state’),
(‘Mackenzie’,’Sullivan’,’DATA’,’SO’,’In state’),
(‘Caleb’,’Taylor’,’UNK’,’FR’,’In state’),
(‘William’,’Taylor’,’IS’,’SR’,’In state’),
(‘Chloe’,’Thomas’,’FIN’,’SO’,’In state’),
(‘Gabriel’,’Thomas’,’LSA’,’SR’,’Out of state’),
(‘Destiny’,’Thompson’,’DATA’,’SR’,’Out of state’),
(‘Owen’,’Thompson’,’DATA’,’SR’,’In state’),
(‘Riley’,’Torres’,’FIN’,’JR’,’In state’),
(‘Kiara’,’Turner’,’DATA’,’SR’,’Out of state’),
(‘Imani’,’Walker’,’LSA’,’JR’,’In state’),
(‘John’,’Walker’,’FIN’,’SO’,’In state’),
(‘Victoria’,’Wallace’,’IS’,’SO’,’In state’),
(‘Aria’,’Ward’,’FIN’,’SO’,’In state’),
(‘Eliana’,’Washington’,’IS’,’SR’,’In state’),
(‘Aubrey’,’Watson’,’IS’,’SR’,’In state’),
(‘Grace’,’West’,’IS’,’SR’,’In state’),
(‘Christopher’,’White’,’LSA’,’JR’,’In state’),
(‘Henry’,’White’,’UNK’,’FR’,’In state’),
(‘Alexis’,’Williams’,’FIN’,’FR’,’In state’),
(‘Daniel’,’Williams’,’FIN’,’SO’,’Out of state’),
(‘Brandon’,’Wilson’,’DATA’,’SR’,’Out of state’),
(‘Julian’,’Wilson’,’IS’,’SR’,’In state’),
(‘Emily’,’Wood’,’FIN’,’JR’,’In state’),
(‘Luna’,’Woods’,’IS’,’JR’,’In state’),
(‘Jayden’,’Wright’,’IS’,’FR’,’Out of state’),
(‘Jada’,’Young’,’DATA’,’JR’,’In state’),
(‘Aniyah’,’Bell’,’FIN’,’SO’,’In state’),
(‘Emily’,’Davis’,’DATA’,’SO’,’In state’),
(‘William’,’West’,’LSA’,’FR’,’In state’),
(‘Watson’,’Torres’,’DATA’,’JR’,’In state’),
(‘Kiara’,’Word’,’DATA’,’JR’,’Out of state’),
(‘Aubrey’,’Walker’,’FIN’,’SR’,’In state’),
(‘Jack’,’East’,’UNK’,’FR’,’In state’),
(‘Elijah’,’Morris’,’IS’,’SR’,’In state’),
(‘Daniel’,’Morris’,’LSA’,’SO’,’In state’),
(‘Joe’,’Murphy’,’IS’,’FR’,’In state’),
(‘Benjamin’,’Daniel’,’FIN’,’SO’,’Out of state’),
(‘Isabelle’,’Thames’,’DATA’,’SR’,’In state’),
(‘Josiah’,’Goodman’,’LSA’,’SR’,’In state’),
(‘Josiah’,’Ortiz’,’DATA’,’SR’,’In state’),
(‘Samuel’,’Watson’,’IS’,’JR’,’In state’)
;

select * from student limit 10;

# Offerings
INSERT INTO `university`.`offering` (`OffTerm`, `Format`, `Capacity`, `Course_idCourse`, `Faculty_idFaculty`)
VALUES
(‘Spring’,’Online’,’30’,’10’,’13’),
(‘Fall’,’Online’,’30’,’1′,’3′),
(‘Spring’,’Online’,’50’,’11’,’12’),
(‘Summer’,’Online’,’20’,’10’,’1′),
(‘Spring’,’In person’,’20’,’10’,’1′),
(‘Spring’,’Online’,’20’,’8′,’11’),
(‘Summer’,’Online’,’30’,’1′,’11’),
(‘Summer’,’Online’,’30’,’11’,’6′),
(‘Spring’,’Online’,’40’,’7′,’9′),
(‘Spring’,’Online’,’20’,’11’,’6′),
(‘Summer’,’Online’,’40’,’9′,’6′),
(‘Fall’,’In person’,’50’,’1′,’11’),
(‘Spring’,’Online’,’40’,’11’,’12’),
(‘Spring’,’Online’,’40’,’11’,’6′),
(‘Fall’,’Online’,’40’,’6′,’5′),
(‘Spring’,’In person’,’20’,’11’,’6′),
(‘Fall’,’Online’,’50’,’9′,’12’),
(‘Fall’,’Online’,’20’,’4′,’10’),
(‘Fall’,’In person’,’30’,’5′,’5′),
(‘Fall’,’In person’,’50’,’3′,’13’),
(‘Fall’,’Online’,’30’,’5′,’5′),
(‘Spring’,’Online’,’30’,’6′,’9′),
(‘Spring’,’In person’,’40’,’6′,’9′),
(‘Spring’,’Online’,’50’,’7′,’5′),
(‘Fall’,’Online’,’40’,’6′,’9′),
(‘Fall’,’Online’,’20’,’7′,’9′),
(‘Spring’,’Online’,’50’,’10’,’1′),
(‘Fall’,’In person’,’40’,’8′,’11’),
(‘Fall’,’Online’,’40’,’11’,’12’),
(‘Spring’,’Online’,’40’,’1′,’3′),
(‘Summer’,’Online’,’40’,’3′,’1′),
(‘Fall’,’Online’,’50’,’6′,’5′),
(‘Summer’,’In person’,’20’,’2′,’6′),
(‘Fall’,’In person’,’50’,’9′,’6′),
(‘Spring’,’Online’,’20’,’3′,’1′),
(‘Spring’,’Online’,’20’,’10’,’1′),
(‘Fall’,’In person’,’20’,’7′,’5′),
(‘Fall’,’In person’,’40’,’5′,’5′),
(‘Fall’,’In person’,’40’,’2′,’2′),
(‘Fall’,’Online’,’20’,’3′,’13’),
(‘Fall’,’Online’,’30’,’9′,’2′),
(‘Summer’,’In person’,’30’,’1′,’3′),
(‘Spring’,’In person’,’50’,’7′,’9′),
(‘Summer’,’Online’,’50’,’7′,’9′),
(‘Spring’,’In person’,’40’,’3′,’1′),
(‘Fall’,’In person’,’30’,’3′,’13’),
(‘Fall’,’Online’,’20’,’2′,’6′),
(‘Spring’,’In person’,’30’,’3′,’13’),
(‘Fall’,’In person’,’20’,’1′,’4′),
(‘Spring’,’Online’,’40’,’2′,’6′),
(‘Summer’,’In person’,’50’,’6′,’5′),
(‘Summer’,’Online’,’20’,’10’,’1′),
(‘Summer’,’In person’,’30’,’9′,’2′),
(‘Spring’,’Online’,’20’,’3′,’1′),
(‘Spring’,’In person’,’20’,’9′,’2′),
(‘Fall’,’Online’,’40’,’10’,’13’),
(‘Fall’,’In person’,’20’,’5′,’5′),
(‘Fall’,’Online’,’20’,’10’,’1′),
(‘Spring’,’In person’,’30’,’9′,’2′),
(‘Spring’,’In person’,’30’,’7′,’9′),
(‘Fall’,’In person’,’15’,’1′,’14’),
(‘Fall’,’Online’,’30’,’10’,’15’),
(‘Spring’,’In person’,’15’,’9′,’15’),
(‘Spring’,’Online’,’20’,’11’,’16’),
(‘Fall’,’Online’,’15’,’10’,’16’),
(‘Spring’,’Online’,’20’,’2′,’17’),
(‘Fall’,’In person’,’15’,’5′,’17’),
(‘Summer’,’Online’,’20’,’2′,’13’),
(‘Spring’,’In person’,’15’,’9′,’14’)
;

# Enrollments
# Have about 165 students, about 186 enrollments

INSERT INTO `university`.`enrollment` (`LetterGrade`, `GPAPoints`, `Student_idStudent`, `Offering_idOffering`)
VALUES
(‘B’,’3′,’157′,’64’),
(‘A’,’4′,’158′,’63’),
(‘B’,’3′,’111′,’32’),
(‘B’,’3′,’73’,’7′),
(‘C’,’2′,’119′,’36’),
(‘B’,’3′,’28’,’51’),
(‘B’,’3′,’22’,’45’),
(‘C’,’2′,’58’,’5′),
(‘C’,’2′,’5′,’49’),
(‘B’,’3′,’20’,’27’),
(‘B’,’3′,’100′,’28’),
(‘A’,’4′,’136′,’35’),
(‘A’,’4′,’62’,’36’),
(‘B’,’3′,’117′,’29’),
(‘B’,’3′,’128′,’37’),
(‘C’,’2′,’51’,’6′),
(‘B’,’3′,’59’,’16’),
(‘C’,’2′,’80’,’43’),
(‘B’,’3′,’1′,’31’),
(‘A’,’4′,’160′,’65’),
(‘A’,’4′,’161′,’63’),
(‘F’,’0′,’20’,’15’),
(‘B’,’3′,’134′,’38’),
(‘B’,’3′,’8′,’18’),
(‘C’,’2′,’37’,’16’),
(‘A’,’4′,’10’,’8′),
(‘A’,’4′,’85’,’6′),
(‘A’,’4′,’145′,’1′),
(‘A’,’4′,’119′,’51’),
(‘F’,’0′,’118′,’20’),
(‘C’,’2′,’154′,’63’),
(‘B’,’3′,’155′,’66’),
(‘B’,’3′,’159′,’67’),
(‘B’,’3′,’160′,’64’),
(‘A’,’4′,’59’,’34’),
(‘C’,’2′,’96’,’51’),
(‘B’,’3′,’164′,’63’),
(‘C’,’2′,’165′,’64’),
(‘C’,’2′,’8′,’46’),
(‘B’,’3′,’2′,’37’),
(‘D’,’1′,’29’,’10’),
(‘B’,’3′,’142′,’9′),
(‘A’,’4′,’40’,’44’),
(‘B’,’3′,’111′,’12’),
(‘B’,’3′,’35’,’56’),
(‘A’,’4′,’13’,’12’),
(‘B’,’3′,’129′,’14’),
(‘A’,’4′,’5′,’51’),
(‘B’,’3′,’92’,’47’),
(‘B’,’3′,’106′,’14’),
(‘B’,’3′,’92’,’60’),
(‘C’,’2′,’2′,’6′),
(‘B’,’3′,’22’,’1′),
(‘C’,’2′,’113′,’50’),
(‘A’,’4′,’10’,’49’),
(‘D’,’1′,’109′,’23’),
(‘B’,’3′,’79’,’33’),
(‘B’,’3′,’75’,’10’),
(‘B’,’3′,’92’,’25’),
(‘A’,’4′,’79’,’14’),
(‘C’,’2′,’45’,’59’),
(‘B’,’3′,’161′,’62’),
(‘B’,’3′,’162′,’63’),
(‘C’,’2′,’32’,’31’),
(‘A’,’4′,’111′,’6′),
(‘B’,’3′,’64’,’44’),
(‘B’,’3′,’56’,’51’),
(‘C’,’2′,’126′,’43’),
(‘B’,’3′,’94’,’60’),
(‘A’,’4′,’33’,’49’),
(‘A’,’4′,’153′,’68’),
(‘A’,’4′,’154′,’61’),
(‘D’,’1′,’51’,’27’),
(‘A’,’4′,’151′,’62’),
(‘A’,’4′,’10’,’7′),
(‘B’,’3′,’139′,’59’),
(‘C’,’2′,’108′,’51’),
(‘A’,’4′,’96’,’14’),
(‘F’,’0′,’44’,’8′),
(‘C’,’2′,’39’,’9′),
(‘A’,’4′,’91’,’40’),
(‘B’,’3′,’35’,’47’),
(‘A’,’4′,’69’,’7′),
(‘A’,’4′,’10’,’9′),
(‘B’,’3′,’51’,’34’),
(‘B’,’3′,’59’,’2′),
(‘A’,’4′,’33’,’8′),
(‘A’,’4′,’15’,’4′),
(‘B’,’3′,’150′,’10’),
(‘D’,’1′,’83’,’46’),
(‘B’,’3′,’45’,’55’),
(‘A’,’4′,’1′,’20’),
(‘B’,’3′,’42’,’16’),
(‘B’,’3′,’110′,’58’),
(‘A’,’4′,’57’,’13’),
(‘B’,’3′,’98’,’27’),
(‘B’,’3′,’70’,’38’),
(‘B’,’3′,’152′,’66’),
(‘B’,’3′,’70’,’36’),
(‘A’,’4′,’78’,’8′),
(‘A’,’4′,’75’,’31’),
(‘C’,’2′,’6′,’40’),
(‘B’,’3′,’158′,’65’),
(‘B’,’3′,’153′,’66’),
(‘B’,’3′,’117′,’34’),
(‘B’,’3′,’133′,’21’),
(‘B’,’3′,’59’,’58’),
(‘F’,’0′,’130′,’47’),
(‘B’,’3′,’2′,’19’),
(‘B’,’3′,’131′,’14’),
(‘B’,’3′,’164′,’61’),
(‘F’,’0′,’163′,’65’),
(‘B’,’3′,’79’,’5′),
(‘B’,’3′,’10’,’8′),
(‘A’,’4′,’4′,’14’),
(‘F’,’0′,’122′,’60’),
(‘F’,’0′,’84’,’2′),
(‘B’,’3′,’31’,’42’),
(‘A’,’4′,’111′,’10’),
(‘C’,’2′,’162′,’66’),
(‘A’,’4′,’11’,’36’),
(‘B’,’3′,’42’,’1′),
(‘A’,’4′,’26’,’45’),
(‘A’,’4′,’124′,’5′),
(‘A’,’4′,’77’,’16’),
(‘A’,’4′,’120′,’18’),
(‘D’,’1′,’127′,’39’),
(‘A’,’4′,’129′,’9′),
(‘B’,’3′,’29’,’28’),
(‘A’,’4′,’150′,’19’),
(‘C’,’2′,’7′,’35’),
(‘C’,’2′,’70’,’57’),
(‘F’,’0′,’113′,’31’),
(‘A’,’4′,’47’,’46’),
(‘B’,’3′,’118′,’51’),
(‘C’,’2′,’15’,’57’),
(‘A’,’4′,’77’,’45’),
(‘B’,’3′,’33’,’55’),
(‘B’,’3′,’103′,’7′),
(‘A’,’4′,’128′,’44’),
(‘B’,’3′,’86’,’49’),
(‘B’,’3′,’132′,’3′),
(‘B’,’3′,’55’,’35’),
(‘B’,’3′,’65’,’33’),
(‘B’,’3′,’37’,’55’),
(‘B’,’3′,’143′,’40’),
(‘A’,’4′,’96’,’20’),
(‘A’,’4′,’116′,’38’),
(‘B’,’3′,’98’,’12’),
(‘B’,’3′,’72’,’43’),
(‘B’,’3′,’161′,’69’),
(‘F’,’0′,’163′,’65’),
(‘C’,’2′,’39’,’27’),
(‘C’,’2′,’114′,’19’),
(‘C’,’2′,’71’,’57’),
(‘B’,’3′,’70’,’9′),
(‘B’,’3′,’146′,’54’),
(‘F’,’0′,’30’,’38’),
(‘B’,’3′,’69’,’10’),
(‘C’,’2′,’120′,’25’),
(‘A’,’4′,’131′,’34’),
(‘F’,’0′,’135′,’8′),
(‘B’,’3′,’45’,’24’),
(‘A’,’4′,’152′,’61’),
(‘B’,’3′,’164′,’67’),
(‘B’,’3′,’163′,’66’),
(‘A’,’4′,’156′,’62’),
(‘C’,’2′,’14’,’38’),
(‘C’,’2′,’151′,’61’),
(‘B’,’3′,’114′,’34’),
(‘D’,’1′,’32’,’3′),
(‘B’,’3′,’152′,’64’),
(‘B’,’3′,’101′,’17’),
(‘D’,’1′,’59’,’6′),
(‘B’,’3′,’158′,’67’),
(‘C’,’2′,’83’,’15’),
(‘A’,’4′,’99’,’12’),
(‘B’,’3′,’115′,’5′),
(‘C’,’2′,’61’,’56’),
(‘C’,’2′,’45’,’1′),
(‘B’,’3′,’165′,’65’),
(‘C’,’2′,’161′,’68’),
(‘C’,’2′,’155′,’61’),
(‘D’,’1′,’152′,’63’),
(‘B’,’3′,’156′,’68’),
(‘A’,’4′,’157′,’67’)
;

— MySQL Workbench Forward Engineering

SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0;
SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0;
SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE=’ONLY_FULL_GROUP_BY,STRICT_TRANS_TABLES,NO_ZERO_IN_DATE,NO_ZERO_DATE,ERROR_FOR_DIVISION_BY_ZERO,NO_ENGINE_SUBSTITUTION’;

— —————————————————–
— Schema university
— —————————————————–
DROP SCHEMA IF EXISTS `university` ;

— —————————————————–
— Schema university
— —————————————————–
CREATE SCHEMA IF NOT EXISTS `university` ;
— —————————————————–
— Schema university
— —————————————————–
DROP SCHEMA IF EXISTS `university` ;

— —————————————————–
— Schema university
— —————————————————–
CREATE SCHEMA IF NOT EXISTS `university` ;
USE `university` ;

— —————————————————–
— Table `Student`
— —————————————————–
DROP TABLE IF EXISTS `Student` ;

CREATE TABLE IF NOT EXISTS `Student` (
 `idStudent` INT NOT NULL AUTO_INCREMENT,
 `stdFirstName` VARCHAR(45) NULL,
 `stdLastName` VARCHAR(45) NULL,
 `stdMajor` VARCHAR(45) NULL,
 `stdStanding` VARCHAR(45) NULL DEFAULT NULL,
 `stdResidence` VARCHAR(45) NULL,
 PRIMARY KEY (`idStudent`))
ENGINE = InnoDB;

— —————————————————–
— Table `Faculty`
— —————————————————–
DROP TABLE IF EXISTS `Faculty` ;

CREATE TABLE IF NOT EXISTS `Faculty` (
 `idFaculty` INT NOT NULL AUTO_INCREMENT,
 `FacFirstName` VARCHAR(45) NULL,
 `FacLastName` VARCHAR(45) NULL,
 `FacRank` VARCHAR(45) NULL,
 `FacStartDate` DATE NULL,
 PRIMARY KEY (`idFaculty`))
ENGINE = InnoDB;

— —————————————————–
— Table `Course`
— —————————————————–
DROP TABLE IF EXISTS `Course` ;

CREATE TABLE IF NOT EXISTS `Course` (
 `idCourse` INT NOT NULL AUTO_INCREMENT,
 `CourseDept` VARCHAR(45) NULL,
 `CourseNumber` INT NULL,
 `CourseDesc` VARCHAR(45) NULL,
 `CourseUnits` INT NULL,
 PRIMARY KEY (`idCourse`))
ENGINE = InnoDB;

— —————————————————–
— Table `Offering`
— —————————————————–
DROP TABLE IF EXISTS `Offering` ;

CREATE TABLE IF NOT EXISTS `Offering` (
 `idOffering` INT NOT NULL AUTO_INCREMENT,
 `OffTerm` VARCHAR(45) NULL,
 `Format` VARCHAR(45) NULL,
 `Capacity` INT NULL,
 `Course_idCourse` INT NOT NULL,
 `Faculty_idFaculty` INT NOT NULL,
 PRIMARY KEY (`idOffering`, `Course_idCourse`, `Faculty_idFaculty`),
 INDEX `fk_Offering_Course1_idx` (`Course_idCourse` ASC),
 INDEX `fk_Offering_Faculty1_idx` (`Faculty_idFaculty` ASC),
 CONSTRAINT `fk_Offering_Course1`
   FOREIGN KEY (`Course_idCourse`)
   REFERENCES `university`.`Course` (`idCourse`)
   ON DELETE NO ACTION
   ON UPDATE NO ACTION,
 CONSTRAINT `fk_Offering_Faculty1`
   FOREIGN KEY (`Faculty_idFaculty`)
   REFERENCES `university`.`Faculty` (`idFaculty`)
   ON DELETE NO ACTION
   ON UPDATE NO ACTION)
ENGINE = InnoDB;

— —————————————————–
— Table `Enrollment`
— —————————————————–
DROP TABLE IF EXISTS `Enrollment` ;

CREATE TABLE IF NOT EXISTS `Enrollment` (
 `idEnrollment` INT NOT NULL AUTO_INCREMENT,
 `LetterGrade` CHAR(1) NULL,
 `GPAPoints` INT NULL,
 `Student_idStudent` INT NOT NULL,
 `Offering_idOffering` INT NOT NULL,
 PRIMARY KEY (`idEnrollment`, `Student_idStudent`, `Offering_idOffering`),
 INDEX `fk_Enrollment_Student_idx` (`Student_idStudent` ASC),
 INDEX `fk_Enrollment_Offering1_idx` (`Offering_idOffering` ASC),
 CONSTRAINT `fk_Enrollment_Student`
   FOREIGN KEY (`Student_idStudent`)
   REFERENCES `Student` (`idStudent`)
   ON DELETE NO ACTION
   ON UPDATE NO ACTION,
 CONSTRAINT `fk_Enrollment_Offering1`
   FOREIGN KEY (`Offering_idOffering`)
   REFERENCES `university`.`Offering` (`idOffering`)
   ON DELETE NO ACTION
   ON UPDATE NO ACTION)
ENGINE = InnoDB;

USE `university` ;

SET SQL_MODE=@OLD_SQL_MODE;
SET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS;
SET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS;

Effect of interest rates on bond value and the ripple effect

/0 Comments/in /by

Question

Changes in value of financial assets, and bonds in particular, is the topic for this discussion. The US Federal Reserve Board (the Fed) has increased interest rates, specifically the federal funds rate (the rate banks charge other banks, usually for overnight loans).

Fed interest rate today 2022-present: The Fed’s latest moves in an era of soaring inflation

Rate hikes 2022-present

Meeting date

Rate change

Target range

March 15-16, 2022

+25 basis points

0.25-0.5 percent

May 3-4, 2022

+50 basis points

0.75-1 percent

June 14-15, 2022

+75 basis points

1.50-1.75 percent

July 26-27, 2022

+75 basis points

2.25-2.5 percent

Sept. 20-21, 2022

+75 basis points

3-3.25 percent

Nov. 1-2, 2022

+75 basis points

3.75-4 percent

Dec. 13-14, 2022

+50 basis points

4.25-4.5 percent

Jan. 31-Feb. 1, 2023

+25 basis points

4.5-4.75 percent

March 21-22, 2023

+25 basis points

4.75-5 percent

May 2-3, 2023

+25 basis points

5-5.25 percent

July 25-26, 2023

+25 basis points

5.25-5.5 percent

Source: Fed’s board of governors

As you can see from the data above, interest rates increased by 5% between March 2022 and July 2023. With these changes, there was a ripple effect felt across the economy. The value of bond portfolios held by banks as well as individual investors dropped, the “cost” of borrowing money increased across the board from car loans to mortgage loans as well as business loans.

For your discussion this week, I’d like for you to pick a sector or topic and discuss the impact of the interest rate increases. Below are examples of directions you could take:

Personally (Never put personal info in the post that you are uncomfortable with!! Speak in generalities if you prefer, the point is to apply the interest rate change impacts to your post.

How have the interest rate changes impacted on your purchase decisions maybe it is to delay a major purchase (car or house maybe).

What about the buy vs. rent decision? There have been articles on how the relative economics have changed recently.

Have you modified investment allocations in your portfolio or IRA/401K investments?

Banking sector:

Pick a bank that has failed or was purchased by another bank because of problems with their bond portfolio or similar situation. What was the underlying problem at the bank? What was the outcome? Yes, a deeper discussion of SVB is acceptable here as long as you cite an additional outside credible source. The number of troubled banks is limited.

Corporate sector:

Pick a company that may have had to change their plans on raising funds because of required higher coupon rates. Maybe the company has experienced changes in its sales because consumers are holding off on spending.

Maybe discuss a firm that has had a hard time getting loans because of high rates.

There are a lot of different directions you can go with your post, but no matter what you choose (even personal) you MUST have at least one credible outside source (remember that Investopedia and Wikipedia are not considered reliable). As with any discussion, be sure to reread the grading rubric before posting.

Bottom line:

What aspect are you discussing? What changed, and why? How did decisions or outcomes change from what would have been expected without the interest rate increases? The “so what” is where I’m looking for. Go beyond “interest rates went up, bond prices went down”

COMP – 10205 – Data Structure and Algorithm

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QUESTION

You are to complete the starting code that has been provided for aSortedLinkedListthat will

store a collection of items and maintain the order of the items at all times. You will need to
add functionality for the following methods:
• add
• remove
• toString
In case of the add method, the elements must be added in sorted order. When you add the
following words in this order [Bob, Carol, Aaron, Alex, Zaphod], the list when printed using the
toString method will appear as [Aaron, Alex, Bob, Carol, Zaphod]. You may not call any sort
algorithm to achieve this result. Your goal is to ensure that the list is always in a sorted state.
With the remove method, the element specified must be removed from the list and the current
order maintained.
The class provided has been provided as a generic class. It will work with any object data type
class that is Comparable. In your main method you must demonstrate that this works using
two different data types that add elements in random order to the list. The sorting order in all
cases will be ascending alphabetic order (‘A’ at the top, lowest integer at the top, etc…).
Once you have demonstrated that your class can handle two different data types (make one of
them String), you will need to compare the performance of the class against the standard java
ArrayList class. To achieve equivalent functionality with the ArrayList, add each name to
the list and then call Collections.sort method after each each item is added (ythis must be
placed in the loop that is adding the items. This will ensure that we are comparing the same
functionality in both classes (always sorted).

The main program as provided will read names from a text file (baby names) and place the
content into an array. It is suggested you use this data for comparison purposes for the String
data type.
Suggested Steps:
1. Create an add method for the SortedLinkedList<T> class
2. Test the add method from main to ensure elements are added in sorted order. To do
this you will need to complete the toString method.
3. Create a remove method for the SortedLinkedList class
4. Test the remove method from main to ensure elements are removed from the list and
that after removal the list is still in sorted order.
5. Add a Discussion (in a comment) to the top of the file that contains your main method
discussing the results obtained. Answer each of the following questions:
o Do you notice any significant performance difference between the
SortedLinkedList<T> and the ArrayList<T> classes when adding items?
Explain the differences using Big O notation for the different algorithms.
o Do you notice any significant performance difference between these two
collections when removing items? Explain the differences using Big O notation
for the different algorithms.
o When would you choose to use a SortedLinkedList over an ArrayList based
on the results of this assignment?

LSA Discussion Question

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QUESTION

180 words

NOTE: Read the post below and answer any 1 question (out of three). This is an Ethics class.

Instructions

Smartphone driven services like Uber, Lyft, and DoorDash allow customers to quickly and

easily arrange for a ride or get food delivered. At the same time, they also provide just

about anyone with a driver’s license and a working automobile the ability to earn money

as drivers.

For many people who work for these companies, the goal is simply to earn additional

income over and above their primary job. Others prefer the freedom and flexibility of “gig

work,” and may be employed in freelance or contract work in a variety of roles

simultaneously.

At the same time, some people would prefer the stability, benefits, and job protections

normally associated with full-time employment to apply to all workers. In addition, union

leaders, as well as some politicians, have criticized the growing gig economy for reducing

the bargaining power of workers and giving more control to corporations.

Reacting to the rapid growth of the ride-share companies, California passed a law in 2019

that required companies to treat contract workers as employees. In reaction, Uber, Lyft,

and DoorDash threatened to cease operating in the state, and then supported a ballot

initiative known as Proposition 22. Prop 22, as it is known informally, modified the 2019

law by allowing drivers to remain classified as independent contractors but to be eligible

for some benefits, such as better pay and accident insurance (Conger & Browning, 2021).

Prop 22 passed in November 2020 with 59% of the vote, which seemed to settle the issue.

However, in August 2021 a judge in California ruled that Prop 22 was unconstitutional for

several reasons, including a provision that prevented drivers from unionizing (Conger &

Browning, 2021). The ruling will most likely be appealed in the future, meaning that this

controversial issue will be with us for some time to come.

As you write your initial post this week, consider the following questions:

Question 1: Much of this controversy seems to revolve around the

concept of freedom. Should companies like Uber, Lyft, and DoorDash be

free to run their businesses as they see fit? Should drivers have the

freedom to unionize? Whose freedom is more important?

Question 2: Many employers now operate using a combination of full-

time employees and contract workers. From the employers’ perspective,

this allows for greater flexibility, and gives people who don’t want to work

full-time an opportunity to contribute. On the other hand, this

arrangement may also be seen as creating a hierarchy in which the full-

time employees earn better pay and benefits while the contract workers

don’t share equally in the prosperity. From the standpoint of equity, are

companies that rely heavily on contractors treating all their workers

fairly?

Question 3: Have you ever had a gig job? If so, how does that impact your

view of this issue?

Personal loan acceptance

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QUESTION

Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositors) with varying sizes of relationship with the bank. The customer base of asset customers (borrowers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business. In particular, it wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).

A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise smarter campaigns with better target marketing. The goal is to use k‐NN to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign.

The dataset mlba::UniversalBank contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer’s relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.

Partition the data into training (60%) and holdout (40%) sets.

  1. Consider the following customer: Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education = 2, Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1, and Credit Card = 1. Perform a k‐NN classification with all predictors except ID and ZIP code. Remember to define categorical predictors with more than two categories as factors (for k‐NN, to automatically handle categorical predictors). Create KNN model with k=1. How would this customer be classified?

Use set.seed(1) for training.

B. What is a choice of k that balances between overfitting and ignoring the predictor information? Use 5‐fold cross‐validation to find the best k.

Use set.seed(123) for cross validation

The best K for the model is saved in model$bestTune

C. Show the confusion matrics for the training and holdout data that results from using the best k. Comment on the differences and reasons.

The code example below shows how to produce the confusion matrix for the training set

cm <- confusionMatrix(predict(model, train.df), train.df$Personal.Loan)

D. Consider the following customer: Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education = 2, Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1 and Credit Card = 1. Classify the customer using the best k.

few questions related to R

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Question

The Prostate Dataset

The prostate dataset comes from a study on 97 men with prostate cancer who were due to receive radical prostatectomy.

The data contain the following variables:

lcavol: log(cancer volume in cm3)

lweight: log(prostate weight in gm)

age: age in years

  • lbph: log(benign prostatic hyperplasia amount)

svi: seminal vesicle invasion

lcp: log(capsular penetration)

Gleason: Gleason score

  • pgg45: percentage Gleason scores 4 or 5

lpsa: log(prostate specific antigen in ng/mL)

Question 1

  • Validate that the prostate data frame contains 97 observations.
    Hint: First install the faraway package (if you haven’t already) as instructed on Lesson 1, Slide 49. The following R statement will load the prostate data frame:

data(“prostate”, package = “faraway”).

Use the nrow() function to see how many overvaluations (rows) the data frame has. For example: the following statement prints the number of observations in the car data frame: nrow(cars).

  • Question 2

Calculate descriptive statistics of each of the variables.
Hint: Use the summary() function. For example: summary(cars).

Question 3

  • Create a new data frame that includes the following variables: lcavol, lweight, age and lpsa.
    Use this new data frame for all questions below.

Hint: In the following example, we select two variables (agegp and alcgp) from the esoph data frame and name the new data frame esophSubDf

esophSubDf <- esoph[c(“agegp”, “alcgp”)]

  • Question 4

Calculate descriptive statistics of each of the variables using the new data frame.

Question 5

  • Create a scatter plot matrix for all the variables using the new data frame.

Hint: Use the pairs() function (see Lesson 2, Slide 50).

Question 6

  • Create a (Pearson) correlation matrix for all the variables.
    Hint: Use the cor() function (see Lesson 2, Slide 48).

Question 7

Show the same matrix again, but round the correlations (use two decimal places).

  • Hint: Use the round() function. The following example calculates the correlation matrix for the cars data frame and rounds the numbers:
    round(cor(cars),2)

Question 8

Create a regression model:
The predictor variable (X) should be lpsa.
The outcome variable (Y) should be lcavol.
Show the summary of the model.

Hint: Use the lm() and summary() functions (see Lesson 2, Slide 51).

Question 9

Visualize the two variables and the model you just created by doing the following:

Create a scatter plot. Put lcavol in the y-axis and lpsa in the x-axis. Include the regression line and label the axis.

Hint: See Lesson 2, Slide 52.

Question 10

Update the regression model by adding a second predictor: age
Show the regression model summary

IT404 web desgin Saudi electronic university Website Design Questionx

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QUESTION

Instructions:

You must submit two separate copies (one Word file and one PDF file) using the Assignment Template on Blackboard via the allocated folder. These files must not be in compressed format.

It is your responsibility to check and make sure that you have uploaded both the correct files.

Zero mark will be given if you try to bypass the SafeAssign (e.g. misspell words, remove spaces between words, hide characters, use different character sets, convert text into image or languages other than English or any kind of manipulation).

Email submission will not be accepted.

You are advised to make your work clear and well-presented. This includes filling your information on the cover page.

You must use this template, failing which will result in zero mark.

You MUST show all your work, and text must not be converted into an image, unless specified otherwise by the question.

Late submission will result in ZERO mark.

The work should be your own, copying from students or other resources will result in ZERO mark.

Use Times New Roman font for all your answers.

Question One

Explain the differences between programming on the client and server sides, and what languages are used for each?

2 MarksLearning Outcome(s):
Recognize and evaluate a range of real-world web design approachesQuestion Two

JavaScript, HTML, CSS, and other web design and production processes are all affected by the progressive enhancement. Explain what is the purpose of PE, and what strategies are involved? 

2 MarksLearning Outcome(s):
Recognize and evaluate a range of real-world web design approachesQuestion Three

Describe the different ways to specify a web page’s URL and when they should be used? Give an example for each of them.

2 MarksLearning Outcome(s):
Identify most HTML tags and CSS properties and use a text editor to construct the basic HTML and CSS structure for a webpage. Question Four

Create a webpage that display your name and ID using the most significant heading. Also, use the university logo as a hyperlink to the SEU website.

Important notes:

1.You should copy and paste the “HTML script” to answer this question. DON’T take screenshots for your HTML script. It must be an editable script.

2. Take a screenshot of your output web page and paste it as a part of your answer.