Project Cover Sheet For College-Level Math Courses At Howard ✓ Solved

Project Cover Sheet For College Level Math Courses At Howard Community

Project Cover Sheet for College Level Math Courses at Howard Community College To be successful on your project you must: · Read and follow instructions carefully. · If a grading rubric is provided, review the grading rubric carefully to ensure you are familiar with the expectations for each section. · If you need clarification about any part of this project, please be sure you ask your instructor in a timely manner. · Write clearly, using appropriate terminology and accurate mathematical notation. College-level writing is expected, as is the use of correct grammar. · If you need help with writing, feel free to use the HCC Writing Center: For further information, see the HCC Web page under the heading “Writing Center†or call the Writing Center at (.

PGCC students at the Laurel College Center should see the PGCC Writing Center for assistance. · Submit a neat, professional report typed (double-spaced) using your choice of word processing software (including a mathematical notation package) and including printouts and diagrams from your choice of graphing application, computer algebra software, or statistical software (as applicable). The use of appropriate technology is expected throughout the project. · In particular, embedded “complete†graphs or charts and/or computer printouts will be expected as part of the report. Hand-drawn graphs are not acceptable. · Hand calculations (if applicable to the project requirements) should be scanned and included as an appendix at the end of the report. · Getting help: Your instructor will indicate to you the type of help that you may receive on this project.

Carefully read the instructions below. All explanations must be written in your own words. · Submission guidelines: · Projects should be submitted electronically as a pdf file. · To maintain the maximum score on this project, it is expected that students go “above and beyond†the minimum expectations of the project especially as it pertains to a professional report. General Education Requirements MATH 122: Ideas in Mathematics The projects for this course are primarily designed as an assessment tool to determine if students have obtained certain skills expected to meet General Education requirements. Completion of this course assumes that three general education goals have been met: Critical Thinking (CT), Scientific and Quantitative Reasoning (SQR), and Technological Competency (TC).

As a trial, questions related to each of these goals are included in these projects and will carry the labels as listed above. Each General Education goal is assessed through the use of a rubric designed by faculty members of the college. It is important that you read through the rubrics carefully. The rubrics will be used to grade any question on the project with the labels (TC, SQR, CT) next to the question. These rubrics can be found under the document sharing tab when you sign into MyMathLab.

Project 1 Directions The questions without a label will be graded using a point system as follows: 4 points: No errors 3 points: One minor error 2 points: More than one error, but correct strategy 1 point: incorrect strategy 0 points: Problem not attempted Note: Answers to problems without work that are incorrect will receive a score of 0. It is expected that this project is submitted as a professional report. You will be given points based on the quality/professionalism of the report. This will be the only question that is worth three points and will be scored as follows: 3 points: Project is very easy to read and flows logically. There are no typographical or grammatical errors. Directions from the cover sheet are carefully followed. 2 points: Project does not read very clearly. There are minor typographical and grammatical errors. A few directions from the cover sheet were not followed. 1 point: Project is poorly written and difficult to read. Submission appears unprofessional with many directions not followed. Your instructor will provide a scoring sheet so that you will be able to see where errors were made (if any). DO NOT type the problems in your report. Just number each problem and show all necessary work for each problem. Name _________________________________________________ Score ______/75 Due _________________________________________________

Project 1: Sets & Probability 1. (SQR 3 parts b and c) A landlord has 12 applicants that have applied for an apartment. The landlord uses the FICO score as a primary indicator. To obtain the FICO scores of the twelve applicants using a simulation, use the RandInt feature of your graphing calculator. To get there, do the following calculator steps: MATH, Right arrow three times, the down arrow four times to RandInt, Hit Enter. Type in the command RandInt(300, 850, 12). Hit enter three times. You should have three lists. Use the third list of numbers and write those numbers (in any order) in your report with the names below attached to a score. Bob S.: Dan S.: Julie A.: Samantha B.: Joe M.: Mike A.: Dan B.: Suzanne C.: Amanda M.: Derek K.: Mike N.: Ray O.: Let set A = {x|applicant has credit score above 600} Let set B = {x|applicant has credit score below 550} (4) a. Identify the applicants in sets A and B using the roster method. (4) b. Do you believe it is reasonable for this landlord to only accept applicants in Set A? Explain. (4) c. Let C = (B A)’. List the people in set C, if any. Applicants from set C may be considered if s/he pays two months’ rent plus a security deposit in advance. What are some conclusions that can be made if the landlord considered these applicants? Explain. (Show work!) 2. A lender has 60 applications to view to decide which low-income resident should receive a loan. 15 applicants only have at least 3 credit cards that are “maxed out” (M) 12 applicants only have at least one “120 days late” notice on their credit report (L) 8 applicants only have filed Chapter 13 bankruptcy (F) 10 applicants have at least 3 credit cards that are “maxed out” (reached its credit limit) & have filed Chapter 13 bankruptcy 3 applicants have at least 3 credit cards that are “maxed out”, have filed Chapter 13 bankruptcy, & have at least one “120 days late” notice on their credit report 7 applicants have at least one “120 days late” notice on their credit report & have filed for Chapter 13 bankruptcy Two applicants fall into none of these categories. (4) a. Use appropriate technology to create a three set Venn Diagram. All circles must be labeled and numbers within each region should be clearly identifiable. (4) b. Determine how many applicants have at least one “120 days late” notice on their credit report. (Show work!) (4) 3. (SQR 1) A bank offers various loans to its clients. Let A = the applicant has been approved for a business loan of $100,000 or more. Let B = the applicant has a full-time job. a. Interpret the meaning of P(B|A) in context of the problem. b. Interpret the meaning of P(A|B) in context of the problem. (8) 4. A community activist surveys a community that has been impacted significantly by high mortgage APRs. He would like to volunteer to help his community improve their FICO scores so that they could qualify for lower APRs on their homes. (4 point score will be doubled) FICO score over 600 FICO score 600 or below Mortgage Rate > 5% Mortgage Rate

Sample Paper For Above instruction

In the context of the financial and statistical problems presented, this paper aims to analyze and interpret data related to credit scoring, risk assessment, and decision-making processes in financial contexts. The major focus areas include evaluating the use of probability, set theory, and technological tools to make informed decisions, as well as understanding how credit scores influence financial products and personality assessment through case studies and real-world applications.

Introduction

The modern financial landscape heavily relies on quantitative analysis to assess risks and make lending decisions. One of the primary tools used by lenders and insurers is the FICO credit score, which ranges from 300 to 850 and influences loan approvals, interest rates, and insurance premiums. Understanding the properties, implications, and technological methods for analyzing these scores is crucial for financial decision-making.

Analysis of Set Theory and Probability Applications

The first problem illustrates the use of simulation and set theory to evaluate applicants based on credit scores. By generating a random list of FICO scores for 12 applicants, we can categorize them into sets based on thresholds (>600 for favorable and

The second problem employs Venn diagrams to visualize overlapping categories among loan applicants—those with maxed-out credit cards, late payments, and bankruptcy filings. Visual representation helps to clarify the number of applicants in combined categories and assists in estimating the total number of applicants with overdue payments. These applications highlight the value of technology in efficiently analyzing complex overlaps in data sets.

Conditional Probability and Financial Decisions

Interpretations of conditional probabilities, such as P(B|A) and P(A|B), are essential for understanding the dependencies in financial approval scenarios. For instance, P(B|A) reflects the probability that an approved applicant has a full-time job, given the approval for a substantial loan, guiding lenders on the significance of employment status in approval processes. Conversely, P(A|B) helps in assessing the likelihood that a full-time employee is approved, informing potential risk levels.

Community Financial Behavior and Risk Analysis

The community survey data concerning mortgage APRs and FICO scores illustrate how community-wide data can be used to evaluate risk factors. Probability calculations reveal the likelihood of a person having a FICO score below or above 600 within the community, emphasizing the need for targeted interventions. The calculation of odds further supports understanding the relative likelihood of different financial scenarios, which can inform community programs aimed at improving credit health.

Probability in Insurance and Error Detection

The analysis of error rates in credit reports—claiming that 5 out of 80 reports contain errors—is a classic binomial probability scenario. Calculating the probability of exactly two error reports out of six reviewed demonstrates the application of the binomial distribution in quality control. Additionally, the expectation calculation for an insurance company based on accident probabilities and costs emphasizes how probability models inform risk and profit estimates in insurance underwriting.

Technology and Online Resources

Utilizing online tools like myFICO.com provides critical insights into scoring models and personal credit management strategies. The case of identity theft demonstrates the significance of monitoring credit reports and how digital resources enable individuals to understand and react to fraud effectively. Moreover, the strategies for managing credit card balances to improve FICO scores exemplify how online educational resources serve to inform better financial practices.

Conclusion

Overall, integrating statistical, probabilistic, and technological tools enhances decision-making in finance. By understanding set theory applications, probability calculations, and leveraging online resources, individuals and institutions can better manage their financial risk, improve credit health, and make informed choices. The careful analysis of real-world scenarios demonstrates the importance of quantitative reasoning in achieving financial stability and growth.

References

• FICO. (2022). About your FICO Score. Retrieved from https://www.myfico.com/credit-education/what-is-a-fico-score
• Experian. (2021). Identity Theft: Protecting Your Credit. Retrieved from https://www.experian.com/blogs/ask-experian/identity-theft/
• Investopedia. (2023). Understanding Binomial Distribution. Retrieved from https://www.investopedia.com/terms/b/binomialdistribution.asp
• U.S. Federal Reserve. (2022). Consumer Credit and Credit Scores. Federal Reserve Bulletin. Washington, D.C.
• American Bankers Association. (2023). Loan Approval Processes. Credit Risk Management Report.
• Sargent, T. (2020). Risk and Insurance Modeling. Pearson Publishing.
• Bankrate. (2023). How to Improve Your Credit Score. Retrieved from https://www.bankrate.com/finance/credit-cards/how-to-improve-credit-score/
• Federal Trade Commission. (2022). Consumer Information: Protecting Against Identity Theft. https://consumer.ftc.gov/articles/how-identity-theft-occurs
• World Bank. (2021). Financial Inclusion and Credit Scoring. Global Financial Development Report.
• McKinsey & Company.