Spring 2025, Investments (FIN 346) Session 2 (Wed, Jan 15) Announcements • SmartBook Assignment 1 (Ch 1, 0.5%) is due by 10:00 AM on Fri, Jan 17. • No class on Mon, Jan 20 in observance of Martin Luther King Jr. Day. • Typo in the semester schedule about Exam 1. Should be (Ch 1–3, 13, 30%). • Will update the syllabus with respect to use of artificial intelligence and whether the assign- ments and exams will be on Connect or Blackboard and other typos by next Wednesday. Update • As of 7:00 AM today ◦ 72 students in the class: 36 in M003 and 36 in M004 ◦ 69 had signed in to the class Blackboard site at least once ◦ 29 had earned some extra credit ◦ 6 had completed SmartBook Assignment 1 ◦ 49 had completed ProblemSet Assignment 0 Financial Exchange • Financial Exchange: Exchange of money today for promise/expectation of money in the future. ◦ Borrow $10,000 today and promise to pay back (with interest) in the future. ◦ Buy stock using $30,000 today with the expectation of getting more money back in the future. • A financial asset is created (or used) during a financial exchange. • You would engage in financial exchange only if the utility of what you expect to get back in the future is at least as much as the utility of what you are giving up today. Real and Financial Assets • Real Assets: Car, snow blower, house, airplanes, machinery, education, patent, etc. ◦ Real vs financial (BKM), real vs financial and tangible vs intangible (Investopedia). ◦ Investopedia: Real Asset ◦ Investopedia: Financial Asset ◦ Investopedia: Intangible Asset ◦ Investopedia: Intellectual Property • Financial Assets: Stock, bonds, bank account balances, accounts receivables, prepaid ex- penses, etc. • This course is about investing in (buying and selling) financial assets (aka securities, financial instruments, financial contracts, etc.). Financial Institutions and Markets • Financial institutions (e.g., banks, credit unions, stock brokers, investment bankers, mutual funds, pension funds, insurance companies, etc.) facilitate financial exchange. • Financial markets are made up of individuals and institutions who trade (engage in financial exchange) with each other. • Financial markets channel resources (money) to most productive real assets. In the process, they lead to price discovery, i.e., determination of correct values of assets. • They allow for separation of ownership and management. Investment Preferences • Good companies and good investments: Socially Responsible Investing, ESG investing. • Be skeptical of claims by companies. • Corporate governance and shareholder activism. • Generally, if you don’t like what the company is doing, invest in another one. • Financial Crisis of 2008–2009: Asymmetric information, divergent incentives, hubris. Well explained in the Bird and Fortune videos. Risk and Expected Return • Different market participants have different tolerance for risk. Those with high risk tolerance are willing to invest in high-risk securities with higher expected returns while others invest in low-risk securities with low expected returns. ◦ A higher expected return, a higher return on an average, is the reward (compensation) for risk. But there are no guarantees that investing in a high risk security will always lead to a higher average return, even in a very long run. That’s the risk. Low and High Risk Investments Low and High Risk Investments Possible Realizations Of Returns Growth of $10,000 Another Possible Realizations Of Returns Growth of $10,000 Investment Mechanics for Individuals • Open an account with a broker such as Schwab, Fidelity, Vanguard, Robinhood. ◦ In some cases, you may be able to invest without going through a broker. Examples: Mutual funds, https://www.treasurydirect.gov. • Fund the account by depositing some money. • Place orders for ETFs, mutual funds, stocks, etc. Later expand to more sophisticated invest- ments. Investment Decisions • Asset allocation: How much in different asset classes (risk-free vs risky; risky: stocks, bonds, cash, real estate, crypto, international vs domestic, etc.) • Security selection: Securities within asset classes (top down) or just selection based on knowl- edge and interest (bottom up). • Collecting information and analyzing to decide when and how much to invest in each security. Investing in Competitive Markets • Prices are correct for the most part. There is very little mispricing. Even if there is, by the time you react to it, it would be too late. Sometimes you will get lucky. Enjoy those moments. Don’t let them go to your head. • Seinfeld - The Stock Tip.
Homework: Web Crawling 1. Objective In this assignment, you will work with a simple web crawler to measure aspects of a crawl, study the characteristics of the crawl, download web pages from the crawl and gather webpage metadata, all from pre-selected news websites. 2. Preliminaries To begin we will make use of an existing open source Java web crawler called crawler4j. This crawler is built upon the open source crawler4j library which is located on github. For complete details on downloading and compiling see htps:/githmub.com/yasserg/rzwler4j Also see the document “Instructions for Installing Eclipse and Crawler4j” located on the Assignments webpage for help. Note: You can use any IDE of your choice. But we have provided installation instructions for Eclipse IDE only 3. Crawling Your task is to configure and compile the crawler and then have it crawl a news website. In the interest of distributing the load evenly and not overloading the news servers, we have pre-assigned the news sites to be crawled according to your USC ID number, given in the table below. The maximum pages to fetch can be set in crawler4j and it should be set to 20,000 to ensure a reasonable execution time for this exercise. Also, maximum depth should be set to 16 to ensure that we limit the crawling. You should crawl only the news websites assigned to you, and your crawler should be configured so that it does not visit pages outside of the given news website! meRoot URL01~20NYTimesnytimeshttps://www.nytimes.com21~40WallStreet Journalwsjhttps://www.wsj.com41~60Fox Newsfoxnewshttps://www.foxnews.com61~80USATodayusatodayhttps://www.usatoday.com81~00Los Angeles Timeslatimeshttps://www.latimes.com
MATH4/68052 (GLM’s and Survival Analysis) Coursework 2023-24 The marks awarded for this coursework constitute 20% of the total assessment for the module. Your solutions to the coursework should be no more than several pages long, including text, plots, R output and the code used. It should take, on average, around 10 hours to finish. You are advised to complete all the computing parts first before typing your submission document. Please read all the instructions and advice given below carefully. The submission deadline is 11:00 am on Friday 22 March 2024. Late Submission of Work: Any student’s work that is submitted after the given deadline will be classed as late, unless an extension has already been agreed via mitigating circumstances or a DASS extension The following rules for the application of penalties for late submission are quoted from the Uni- versity guidance on late submission document, version 1.3 (dated July 2019): ”Any work submitted at any time within the first 24 hours following the published submission deadline will receive a penalty of 10% of the maximum amount of marks available. Any work submitted at any time between 24 hours and up to 48 hours late will receive a deduction of 20% of the marks available, and so on, at the rate of an additional 10% of available marks deducted per 24 hours, until the assignment is submitted or no marks remain.” Your submitted solutions should all be in one typed document. This should preferably be prepared using LaTeX or R Markdown. Word is also permissible. For each question you should provide explanations as to how you completed what is required, show your workings and also comment on computational results, where applicable. When you include a plot, be sure to give it a title and label the axes correctly. When you have written or used R code to answer any of the parts, then you should list this R code after the particular written answer to which it applies. This may be the R code for a function you have written and/or code you have used to produce numerical results, plots and tables. R code should also be clearly annotated. Do not use screenshots of R code/output. Instead, to include R code use the verbatim environment. Your file should be submitted through the module site on Blackboard to the Turnitin assessment under Assesment & Feedback entitled ’Coursework (March 2024)’ by the above time and date. The work will be marked anonymously on Blackboard so please ensure that your filename is clear but that it does not contain your name and student id number. Similarly, do not include your name and id number in the document itself. Turnitin will generate a similarity report for your submitted document and indicate matches to other sources, including billions of internet documents (both live and archived), a subscription repository of periodicals, journals and publications, as well as submissions from other students. Please ensure that the document you upload represents your own work and is written in your own words. The Turnitin report will be available for you to see shortly after the due date. This coursework should hopefully help to reinforce some of the methodology you have been study- ing, as well as skills in R. The data we will be using for this coursework comprise n = 712 records of the passengers sailing on RMS Titanic that sank in the North Atlantic Ocean on 15 April 1912 after hitting an iceberg. The estimated total number of passengers and crew on board was 2224. The titanic . df data frame for this coursework contains the following variables: ● Survived: 1 = yes, 0 = no ● Pclass: Passenger class - 1 (1st), 2 (2nd), 3 (3rd) ● Sex: ‘male’, ‘female’ ● Age: 1 = child (under 18), 2 = adult (18 to 60), 3 = senior (over 60) ● Parch: Number of parents and/or children on board for a passenger ● Embarked: Port of embarkation - C = Cherbourg, S = Southampton, Q = Queenstown Survived is a binary response variable and the exercise will be to look at logistic regres- sion models which can be used to predict the probability that a passenger survived, given their particular set of covariate values. Parch is to be regarded as a numeric variable, while each of the others are factors. The code in the R script file start . r can be run to load the data and convert the relevant variables to factors in R using the constraint that the first (reference) level is set to zero. 1. Write down the full additive (but no interactions) logistic regression model with a logit link and explain the notation you have used, including the terms in the linear predictor. Fit this model to the data. Present the R ‘summary’ of your fitted model. Explain and comment on the individual Z-tests of the hypotheses that the true parameter values are equal to zero. [5 marks] 2. Fit a reduced model which excludes the variables Parch and Embarked. Perform an analysis of deviance to show that these two variables do not make a significant contribution to the fit. [1 mark] Can this model, now just containing the variables Pclass, Sex, and Age, be reduced any further? Provide statistical evidence for your answer. [3 marks] The following questions are all just based on using the fitted model which includes the three covariates Pclass, Sex, and Age. 3. Calculate the values for a new binary variable in R called pred . surv whose i’th element is equal to 1 ifˆ(p)(xi ) > 0.5 and equal to 0 ifˆ(p)(xi ) ≤ 0.5. Here, ˆ(p)(xi ) denotes the estimated probability of survival for the i’th sample case who has a vector of covariates xi. [1 mark] Tabulate the values in Survived against pred . surv and calculate the proportion of sample cases correctly classified by the model. Briefly comment on the result. [2 marks] 4. (i) Estimate the odds of survival for an adult female travelling 2’nd class. Briefly comment on the result. [1 mark] (ii) Estimate the odds ratio of survival for a adult female travelling 1’st class to a senior male travelling 3’rd class. Briefly comment on the result. [2 marks] 5. Estimate the probability of survival for an adult female travelling 2’nd class and find an approximate 95% confidence interval for the true value. [5 marks]
FOUNDATION STUDIES MULTIMEDIA Assessment 2 – Title Sequence Due: Week 11 – Friday 4:00 pm Project Overview For this assessment, you will develop a series of skills that introduce you to the fundamental principles of multimedia. During the process of this assessment, you are required to document your creative process from pre-production to post-production explaining the technical, visual, audio and text choices made in the production of your digital animated project. You are required to produce a 20-30 second animated title sequence for a feature film using Adobe After Effects. Your title sequence will include graphic, typographic and audio elements. You will use Adobe Illustrator/Photoshop to create the graphic components and Adobe Audition to prepare the audio. You will document all your progress work in the form. of a digital development book. Your teacher will provide a list of films to choose from. Knowledge and Skills At the completion of this assessment, you should be able to: - Investigate themes and examples of multimedia and kinetic typography projects - Construct justified opinions of analytical tasks - Understand how consideration for degrees of abstraction and simplicity apply to the design process - Document and annotate creative methods for the development of kinetic typographic outcomes - Apply principles and elements of multimedia - Apply principles of typography - Explore software tools for the creation of animation - Apply reflective practice towards the use of stylistic features and visual language in the creation of a kinetic typography product. - Use appropriate design terminology to explain and justify your ideas Research Examine 3 title sequences with a focus on different styles and techniques. Write a description for each including the following: • analysis of the concept • analysis of the visual style. • analysis of the sound Use the research phase to consider and reflect on the type of sequence you want to develop. Make sure to also include technical research in your development book (software tutorials etc.). Concept Development • Brainstorm words, themes and ideas related to the film you choose. • Develop 2 concepts that explore the main themes of your chosen film. • Choose the best concept and develop a comprehensive storyboard with a focus on camera angles & movements. • Draw the graphic components for your title sequence. • Draw the background for your title sequence. Design Refinement • Produce your graphic components and backgrounds in Adobe Illustrator/Photoshop. • Explore at least 3 different font options for your title sequence. • Explore the mise en scène; the overall look and feel of your sequence. • Consider the colour & lighting of your sequence (in reference to your chosen film). • Consider design principles when creating various moving sequences. • Explore different sound effects for the animated content and explain your choices. • Document your digital creative process (screenshots to include in your development book) • Write a brief explanation about your concept. What is the concept and why is it good? IMPORTANT: Annotate your entire design process in your development book. Reflection • Complete a reflection/self-evaluation about your entire process and include it at the end of your development book. References • Reference all research and online content using Harvard Referencing Style. Final Title Sequence 20-30 second Title Sequence for a feature film: • Create your animation in Adobe After Effects. • Export Title Sequence as a Movie File (1920x1080p, h.264 mp4) using Adobe Media Encoder. PROJECT TIMELINE Week Assessment 2 – Title Sequence Completed 6 Interpret & Research Review the brief and highlight keywords. Clarify the requirements of the brief Research 3 examples of title sequences with a focus on different styles and techniques. Research required technical skills and knowledge for your project. 7/8 Concept Development Brainstorm words, themes and ideas related to chosen film. Develop 2 concepts exploring themes of chosen film. Develop a comprehensive storyboard with a focus on camera angles & movements. Draw the graphic components and background. Annotate your process. 9 Design Refinement Produce your graphic components and background in Adobe Illustrator/Photoshop. Explore at least 3 different font options for your title sequence. Explore the mise-en-scene; the overall look and feel of your sequence. 10 Design Refinement Consider the colour and lighting of your sequence. Explore different sound effects for the animated content. Explain your choices. Capture screenshots of your digital creative process to include in your development book. Annotate your process. 11 Development Book Collate all development work into one multi-page PDF document. 11 Final Title Sequence Review movie sequencing, visual language, and timeline Export the final file as a Movie File (1920x1080p, h.264 mp4) using Media Encoder 11 Reflect Complete reflection and include at the back of your development book. Reference all research and online content using Harvard Referencing Style. 11 Submit onto Canvas Submit Development Book and Movie File (1920x1080p, h.264 mp4) Submission Requirements Assessment 2 – Title Sequence *This assessment is worth 30% of the overall mark for this course. Due - Week 11 – Friday 4:00 pm Please do not forget to include a cover page at the start of your development book with your name, student number, and assessment title. Task Submission Format Individual Task Deadline 2.1 Development Book Multipage – PDF Document (Landscape Format) Week 11 – Friday 4:00 pm 2.2 Final Title Sequence Movie File (1920x1080p, h.264, mp4) Week 11 – Friday 4:00 pm 2.3 After Effects File AEP Project Folder incl. all linked assets and AEP file. Week 11 – Friday 4:00 pm Note: It might not be possible to submit your After Effects Folder including assets and AEP file to Canvas due to large file sizes. Your teacher will discuss alternative submission options if needed. AI (Artificial Intelligence) tools cannot be used in the completion of this assessment task.
INT0067 Physics, Engineering and Applied Mathematics Laboratory Exercise - Tensile Testing The purpose of this laboratory exercise is to familiarise you with the typical tensile test method and its application to metals and polymers. The materials tested in this experiment are steel, copper, acrylic (PMMA), and polypropylene. Each specimen used in the test is shaped as shown in Figure 1. The dimensions of each specimen will be carefully measured before testing so that the tensile stress (σ) and strain (ε) could be determined from the data recorded during the test. Figure 1 Test specimen with dimensions l, w, and t. For the test itself, each specimen will be secured into the INSTRON machine and loaded until failure. While experimenting, you should take notes and record your observations. What happens to the test-piece during the test? What changes in shape can be observed? What is the nature of the final failure (how does the piece break)? You can then use the data provided to produce a graph of tensile stress vs. strain for each material. From this graph, you can measure quantities such as the yield stress and ultimate tensile strength of the specimens supplied (and perhaps Young’s modulus, where possible). The data also contains final measurements for each specimen (where possible), which you can use to calculate the % elongation and % reduction in area of each material. Think about what each of the above quantities tell you about the material properties of each specimen (remember your materials vocabulary!) and use your graphs and data to make comparisons and draw conclusions in your lab report. Think also about applications for the materials you have tested, based on your conclusions. For example, if you were to fashion a door handle out of one of the materials tested, which would be the most appropriate and why? You must submit your own formal lab report including a brief description of the experimental procedure, your measurements and the graphical results, and an analysis/interpretation of the results. Detailed guidance on writing the report is given below. You must also complete the laboratory worksheet during the lab session to evidence your work, and include an Excel spreadsheet with all analysis. Your report will consist of: Laboratory Worksheet (scan or photograph and include as separate file or as an image in your report) Written Report (.pdf, .doc, .docx, etc.) Spreadsheet with all calculations (.xlsx or other format) Late submissions will be subject to penalty. INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed) On successful completion of this module you should be able to: Module Specific Skills and Knowledge: 2 apply basic concepts in the analysis of mechanical, electrical and thermal problems 5 recognise when particular techniques are used in a variety of mathematical or engineering situations Discipline Specific Skills and Knowledge: 6 interpret results of laboratory experiments 7 demonstrate understanding of theoretical principles through application to problems 8 interpret answers to problems with appropriate accuracy Personal and Key Transferable/ Employment Skills and Knowledge: 9 apply appropriate methods to address a well-defined engineering problem 10 communicate effectively in the written form Writing your Lab Report Please use the guidance below to help you write your Tensile Testing lab report. The report should be word-processed using Arial Font, size 12, with 1.5 line spacing and standard margins and you should include your student numbers in the header of each page. You should write your report in the past tense and avoid the use of “we” or “I”. Important Notice Regarding AI Use This piece of work can be “AI-supported”. This means you may use generative AI e.g. ChatGPT ethically and responsibly e.g. to produce a suggested outline or to summarise a source. However, you must not copy directly from an AI-generated source (or any other source, including the work of other students) when writing your report – it must be written in your own words. Copying from an AI-generated source is treated the same as plagiarising any other source, and this is considered academic misconduct. This includes copying text, images, videos or any other material generated by the AI. If you use AI, you must reference it in your report, and also keep a record of all prompts and output from the AI (e.g. copying text or taking screenshots). Your teachers can ask to see this information after you submit your work if they suspect AI misuse. Please see the University of Exeter guidelines on AI use here: https://libguides.exeter.ac.uk/referencing/generativeai; this includes guidelines on how to reference AI use. In addition, whether you have used AI or not, you must add the declaration on the next page at the start of your report. Please copy and paste it into your document, ticking the appropriate boxes.
Anthropology 1: Introduction to Biological Anthropology Spring 2025 This is a study guide organized by lecture covering topics from Lectures 1-12. In preparation for your midterm, you are responsible for understanding concepts at the level discussed in the textbook and lectures. The concepts listed here are not meant to cover all aspects of the course material but rather to help you focus on some specific questions. Similarly, the list of terms is not exhaustive and does not repeat the many terms defined already in your textbook. You are responsible for all material presented in lecture, section, and the lab notebook, even if it does not appear on this study guide. We look forward to seeing you at the review session and happy studying! L1: Introduction to Biological Anthropology THINGS TO KNOW: What are the four fields of Anthropology? What is biological anthropology? SOME TERMS: Linguistics Archaeology Primatology Skeletal biology Paleoanthropology Anthropological Genetics Human Biology L2: Development and History of Evolutionary Theory THINGS TO KNOW: What were the first origin myths and what was the concept of fixity of species? What is the Great Chain of Being? What contribution did John Ray and Carolus Linnaeus make to classification of organisms and understanding our place in nature? How did people interpret the fossil record in the early 17th and 18th centuries? Explain the difference between the Lamarckian and Darwinian theories of evolution How did Lyell, Malthus, and Wallace influence Darwin’s ideas of evolution? What is selective intentional breeding and how did it help Darwin formulate his idea of natural selection? What is the difference between intentional and inadvertent artificial selection? What is differential reproduction? L3: Inheritance of Biological Traits THINGS TO KNOW: What does “survival of the fittest” really mean? Does evolution necessarily mean progress? Are species trying to evolve to get somewhere else and become more complex? Are chimpanzees trying to evolve to become humans? Why or why not? What is the difference between stabilizing, directional and disruptive selection? Give an example of each. What are chromosomes? How many chromosomes do humans have? What is a gene? What is an allele? Give an example. What is the significance of the experiments done by Gregory Mendel in the mid 1800's? What did we learn about the patterns of inheritance from Mendel? What is the difference between mitosis and meiosis? When does crossing-over occur? What happens during crossing-over? How does independent assortment and recombination (crossing-over) contribute to genetic variation? SOME TERMS: meiosis/mitosis gametes allele gene homozygous/heterozygous dominant/recessive/codominant X-linked traits (e.g. hemophilia, color blindness) DNA, RNA mutation DNA-gene-chromosome-genome-mutation epigenetics L4: Modern Evolutionary Theory THINGS TO KNOW: What is meant by the term "carrier" with regard to sex-linked traits? Why is the male more frequently affected in sex-linked traits than females? What causes color blindness? If a homozygous color-blind male mates with a female that is heterozygous normal woman, what percentage on average will they have a color-blind boy? What percentage on average will they have a color-blind girl? What is the only source of new genetic variation? How can a single change in a base alter the production of a protein? Give an example of a point mutation. What causes genetic mutations? How are gene flow and genetic drift potent forces in evolution? What is founder effect and what is a bottleneck in a founder population? SOME TERMS: Point mutation Gene flow Genetic drift Founder effect bottleneck SNP L5: Forces of Evolution THINGS TO KNOW: What are the major types of reproductive isolating mechanisms? How do you end up with two groups being reproductively isolated? Under what circumstances can an adaptive radiation occur? What is punctuated equilibrium? What is deep time? How old is the universe and the earth? SOME TERMS: Adaptive radiation Punctuated equilibrium K-Pg boundary L6: What is a primate I: overview and prosimians THINGS TO KNOW: What is the relevance of tupaias in our understanding of primate evolution? Know modern primate examples for each group. Know the geographic distribution of the group. How did lemurs get to Madagascar? What dictates the location of the foramen magnum across dogs and primates? How basal metabolic rates correspond with body size, and the implications for understanding diet. What adaptations came along with diurnal feeding? Why do nocturnal primates typically have large eyes? How did the shift to frugivory lead to the loss of endogenous vitamin C synthesis? And the evolution of trichromatic vision? What are the key morphological and behavioral features that distinguish these groups from each other? Platyrrhines/NWM vs. Catarrhines/OWM) Lemurs vs. Lorises vs. Tarsiers Ceboids vs. Cercopithecocines Gibbons vs. Siamangs vs. Orangutans Bonobos vs. Chimpanzees vs. Great apes Know the genus name for gorillas, orangutans, chimpanzees, bonobos, and human SOME TERMS: Binocular vision Nocturnal Diurnal Foramen magnum Basal metabolic rate Folivore, frugivore, insectivore L7: What is a primate II: Evolution of Old and New World Monkeys THINGS TO KNOW: How do NWM and OWM differ? What are the locomotor adaptations of primates? Why is “twinning” important for marmosets? What adaptations did they evolve for child rearing? What are the dietary adaptations in different monkey groups related to frugivory, folivory, insectivory, and gumivory? Why does infanticide occur in some primate groups? What are the advantages and disadvantages of monkey sociality? Why is there competition and dominance hierarchy in baboon males? Why do baboon males have big canines, and other indicators of sexual dimorphism? Why are colobines called leaf monkeys? How does sexual dimorphism and social structure relate to one another? And why? Why do hamadryas, geladas, and orangutans have large tufts of hair around their neck? SOME TERMS: Brachiation Multiparous vs. uniparous Sexually dichromatic Sexual dimorphism Bilophodont Estrus Polyandry vs. polygyny Terrestrial vs. arboreal quadrupedalism Infanticide L8: The Evolution of Apes THINGS TO KNOW: Which apes are located in Southeast Asia vs. Africa? What locomotor and skeletal adaptations did apes develop? How are gibbons sexually dimorphic, if at all? What behaviors do they perform. in pairs? What aspects of their social organization influence these physical and behavioral traits? What is the social organization in orangutans? What kind of dimorphic changes do you see in orangutan males during growth and with age? Where in Africa are great apes located? Why do we not have a lot of fossils of early African great apes? What dietary adaptations do gorillas have? Do gorillas exhibit the capacity for tool use? What are the sexual dimorphic differences in gorillas? How do gorillas and chimps locomote? What are the social organizations of gorillas? Do males and females move across groups? SOME TERMS: Y-5 molars Knuckle-walking Tool use Abduction L9 & 10: What is a primate IV: African Great Apes THINGS TO KNOW: What is the function of the ilium? How do the gorilla and human ilia differ in structure and function? What are key characteristics of chimps? What is the social organization of common chimps? Which primate groups exhibit same-sex sexual behaviors (as discussed in class)? How does tool use benefit chimps? What are examples of kin-cooperation in chimps? Describe male troop social behaviors in chimps and the benefit/utility of these group-based social behaviors. What indications of violent behavior. have been observed in common chimps? What kind of tool use has been observed in common chimps? How do chimps learn behaviors like tool use? How do bonobos differ socially from the common chimp? What is the social organization of bonobos? How is bonobos sexuality different vs. other primates? Why is there so much extensive sexuality in bonobos? What is sperm competition and how does this relate to sexual anatomy in apes? SOME TERMS: Kin-cooperation Culture Carnivory Sexuality L11: Dr. Lewis’s Guest Lecture THINGS TO KNOW: Do other mammalian species, aside from humans, exhibit signs of long-term social memory? What was the “Memory of Relationship” hypothesis? Do chimps and bonobos remember the faces of their groupmates overtime? L12: Primate Health and Disease THINGS TO KNOW: Why is it important to study non-human primate health? Do non-human primates exhibit diseases similar to those of human? What are some natural risk factors for non-human primate health? What are the pros and cons of non-human primate captivity? Can primates and humans infect each other with pathogens? What percent of emerging infectious diseases come from primates? What is the One Health concept? SOME TERMS: Emerging infectious disease Reservoir Vector One Health
ICOM 202 Intercultural Communication and Global Citizenship For academic year 2025 This course focuses on how intercultural communication is central to global citizenship, using real and potential communication breakdowns caused by linguistic and cultural diversity. Students will develop skills and strategies to negotiate difference and address communication impasse or conflict productively. Course content In 2025, this course will be delivered primarily on campus, with online accessibility. Most students will attend on campus; however, thel course can be completed online if needed, If you intend to take the course mostly on campus, please select the offering CRN 33001. This course introduces students to concepts, theories and practices of intercultural communication across a range of personal and professional contexts.The course focuses on interpersonal communication and how this process relates to conceptions of culture, intercultural competence and dlobal citizenship. Course learning objectives Students who pass this course should be able to: 1 Analyse linguistic and cultural differences affecting intercultural communication in a globalized world. 2 Critically discuss issues of identity, power, conflict and ethics underpinning global citizenship. 3 Apply concepts and practices of global citizenship to New Zealand's multiethnic society.
Term Project and Homework Assignments Returns to Education ECON 4400 1 Overview Human capital, defined as the skills, knowledge, and abilities that an individual possesses, has been a focal point of economic research in labor, development, and political economy—to name a few. Education and job training are important human capital investments, leading to higher earnings and non-pecuniary bene- fits. Your ECON 4400 project focuses on the former. You will quantify the returns to education, estimating the effect of a year of schooling on an individual’s wage. While economics has developed sound theoret- ical foundations, empirical work on the return to human capital has been at the center of considerable debate. As part of your project, you will explore a part of that debate by replicating (approximately, I have simplified the analysis to a degree) the results of Angrist and Krueger (1991) using the 2022 American Community Survey (ACS). I chose this approach to foster critical thinking and deepen econometric knowledge. Our analysis will also draw upon Bound, Jaeger, and Baker’s (1995) critique of the instrumental variables ap- proach used in Angrist and Kreuger (1991). You are therefore required to read both papers, Angrist and Krueger (1991) and Bound, Jaeger, and Baker (1995). I have posted papers on our Carmen course site Mod- ules page under “Articles for Term Project” . Throughout the term, you will complete parts of the analysis and submit each component as a homework assignment. In doing so, I can assist with your learning of econometrics in practice. Additionally, the home- work assignments enable me to address issues with coding or analysis. For each assignment, you need only to submit what is requested. You will include the tables created for each assignment with your term project. A homework assignment will also ask you to introduce, discuss, and explain particular sections of your term project, e.g., data, regression analysis, results, and econometric methodologies. After I return the assignment, you should edit and expand the section following the outline below, addressing any notes or needed corrections. You will analyze the returns to education in a U.S. state. Refer to Table 1 to see your assigned state. To download your data file, log on to Carmen, go to Modules, scroll toward the bottom of the page, and download the state data file assigned to you. 2 Paper Requirements and Expectations You will write a three to six page analysis (not including tables and can be longer if needed) on the returns to education and submit it at the beginning of class on Friday, 04/18. The paper will include two tables: a table of summary statistics and returns to education estimates (see Sections 3.1, 3.2, and 3.3. You need to attach your do-file with the paper. If you do not submit a working do-file, you will receive, at most, half credit for this assessment. Your do-file needs to be cleaned of any redundant or incorrect commands. The entire do-file needs to be executable. In other words, if you click the execute icon, Stata executes every command without error. Your write-up of the analysis should follow the below general outline–the sub-items do not need to follow the stated order. You must address each enumerated item but can include additional background or support as needed. Your writing needs to flow (does not read as an itemized list), and each paragraph must consist of one key idea with supportive statements (evidence, results, etc.) about that key idea. You must also ensure your writing includes transitions between key ideas (paragraphs). 1. Introduction (a) Discuss the importance and benefit of education in the context of earnings. For background, read the following papers: • “Economic returns to education: What We Know, What We Don’t Know, and Where We Are Going–Some Brief Pointers” by Dickson and Harmon (2011) • “Does Compulsory School Attendance Affect Schooling and Earnings” by Angrist and Krueger (1991) • “Educational Attainment and Quarter of Birth: A Cautionary Tale of LATE” by Barua and Lang (2008) • “Problems With Instrumental Variables Estimation When the Correlation Between the In- struments and the Endogenous Explanatory Variable is Weak” by Bound, Jaeger, and Baker (1995) You can access the papers in the module Articles for Term Project on our Carmen course site’s Modules page. 2. Data and Methodology (a) Cite and discuss the data used for the analysis (b) Discuss the sub-samples used for the analysis, referencing the summary statistics 3. Returns to Education (a) Introduce and discuss the wage equation (b) Discuss OLS return to education (c) Discuss why the OLS estimate for the return to education is biased (d) Discuss Two Stage Least Squares (2SLS) estimator–how does it address the endogeneity prob- lem? (e) Discuss the instrumental variables, including the relevancy and validity requirements (f) Discuss the 2SLS return to education (g) How do your results compare to Angrist and Krueger (1991). Specifically, do your results com- port with the authors’ findings for the 30-39 and 40-49 cohorts? (h) Compare and discuss OLS versus 2SLS estimates. Do the result meet expectations? Explain (Hint: why is the OLS estimator of the returns to education biased?) Discuss the F-statistic from the test for weak instruments. What insights does the test provide regarding the results? 4. Discussion and Conclusion 2.1 Paper Formatting • Font: 11pt Times New Roman font • Margins: One-inch margins (top, bottom, left, and right) • Line spacing: 1.5 lines • Start of new paragraph: Indent (no additional spacing between paragraphs) • Text Alignment: justified • Make sure to include your first and last name on the paper References and Citations - Chicago Style. If you choose to support an argument by drawing on the work of other scholars, you need to follow the below citation and reference style (Chicago). When you cite an article or research paper, you must include a reference section with your paper. Citation and reference examples: In-text citation Reference list Author Year First author’s last name, first author’s first name, second author’s first and last names, third author’s first and last name, . . . , and last author’s first and last name. Year of publications. “Title of article.” Title of Journal, volume number(issue/number, or date/month of publication if volume and issue are absent): page numbers (if any). Example - Parenthetical (Tesseur 2022) Tesseur, W. 2022. “Translation as inclusion? An analysis of international NGOs’ translation policy documents.” Language Problems and Language Planning, 45(3): 261–283. Example - Narrative Piketty and Saez (2003) Piketty, Thomas, and Emmanuel Saez. 2003. “Income Inequality in the United States, 1913–1998.” The Quarterly Journal of Economics, 118(1): 1–41. 2.2 Stata Do-File You will generate one do-file for this project. Each assignment will have you add to your code document (do-file). You must save your do-file at each step of the project (I recommend saving it regularly when working on an assignment). Separate each part using asterisks. For example: ******************** **ECON 4400 Project: Name - Assigned State ******************** ******************** **Homework 1 - Summary Statistics . . .code here . . . ******************** ******************** **Homework 2 - OLS Returns to Education . . .code here . . . ******************** ******************** **Homework 3 - 2SLS Returns to Education . . .code here . . . ******************** 2.3 Data Assignments Table 1: Data (state) Assignments for Term Project (and Homework) Name State FIP State AlAjlouni, Ahmad 19 Iowa Ali, Hafsa 13 Georgia Backlin, Ben 6 California Bobie, Kofi 21 Kentucky Cai, Boxun 49 Utah Campisi, Matthew James 20 Kansas Caracciolo, Isabella Grace 51 Virginia Chen, Gong 48 Texas Dia, Djnda 25 Massachusetts Dohler Rodas, Edison Emilio 44 Rhode Island Duan, Tommy 46 South Dakota Gu, Huajie 9 Connecticut He, Feihuan 55 Wisconsin Hou, Murong 29 Missouri Huo, Yu 24 Maryland Kopocs, Nate 27 Minnesota Lintz, Nicholas Michael 16 Idaho Liu, Renlong 5 Arkansas Lu, Shibo 8 Colorado Ma, Haotian 30 Montana Maokhamphiou, Zhanguosong Jaynarong 28 Mississippi Mendez, Jesse Wayne 33 New Hampshire Oljira, Yemesrach Mulugeta 47 Tennessee Pulsifer, Aiden 11 District of Columbia Shah, Dhruv 42 Pennsylvania Shi, Chloe 15 Hawaii Spicer, Hannah Lauren 34 New Jersey Sun, Xinrui 4 Arizona Tessman, Vija Elizabeth 26 Michigan Warner, Jeffrey 37 North Carolina Williams, Gavin Redmond 17 Illinois Wu, Oliver 35 New Mexico Zhang, Bojia 54 West Virginia Zhang, Guangjie 12 Florida Zhang, Haoyue 36 New York Zhao, Han 31 Nebraska Zhao, Wenzhong 18 Indiana 3 Homework: Putting Together Your Analysis 3.1 Homework 1, Due Friday, 02/07 Overview of assignment and what you will submit: You will generate a table reporting summary statistics of various samples and write one to three paragraphs summarizing and comparing economic variables across different groups. We will compare multiple samples of individuals of various age groups, who reported an income in 2021. The focus of the write-up needs to be on composition of the samples relative to others. You want to focus you write up of how the sample of respondents between the ages 30 to 39 and 40 to 49 year olds compare to one another as well to those between the ages 25 and 64. You will submit a paper copy of your write-up with the summary statistics table and a print-out of your do-file at the beginning of class on Friday, 02/07. What to Submit - three items: 1. A write-up discussing the data source, the samples, and summary statistics. (I have included an example of a write-up of summary statistics below Table 2 for reference.) 2. A table of summary statistics, created using Word or Excel. 3. Attach a printout of your do-file (the entire document) Instructions: You will generate summary statistics for four subsamples. The first subsample consists of all wage and salary workers and self-employed individuals between the ages of 25 and 64 who report a 2021 income (the 2022 ACS reports income from the prior year). The second sample restricts the first one to only wage and salary workers between the ages 25 and 64. The third subsample is comprised of wage and salary workers between the ages 30 and 39. The fourth subsample consists of wage and salary workers between the ages 40 and 49. We will use the latter two samples to estimate the returns to education. Your first homework assignment will require you to complete a process known as data cleaning. Researchers often need to recode or generate new variables from survey data. The below commands will walk you through how to “clean ACS data” to estimate the returns to education and the probability that an individual participates in the labor force. The task of data cleaning is often an arduous one. To cultivate command-based coding and data analytics skills using Stata, I am providing all the code for this portion of the project. In Stata to indicate a range, e.g., tabulate incwage between 20,000 and 40,000, i.e., 20, 000 ≤ incwage ≤ 40, 000, the code is tab incwage if incwage>=20000 & incwage=25 & age=35 Generating an inputed hourly wage and keeping all wage and salary workers and self-employed respondents who earn more than $2/hr. (Tipped worker minimum wage is $2.13–rounded to $2/hr) drop if incwage== . | incwage==999999 | incwage==999998 gen hwage=(incwage/wkswork1)/uhrswork keep if hwage>=2
MATH4/68052 Generalised Linear Models: Coursework (2025) The marks awarded for this coursework constitute 30% of the total assessment for the module. Your solution to the coursework should be reasonably concise - about 10 pages with tables, plots, and code, but there is no penalty if you do exceed this. It should take, on average, about 9 hours to complete all the work, including preparing your final document to be submitted. Please read all the instructions and advice given below carefully. The submission deadline is 10:00 am on Monday 10 March, 2025 . Late Submission of Work: Any student’s work that is submitted after the given deadline will be classed as late, unless an extension has already been agreed via mitigating circumstances or a DASS extension. The following rules for the application of penalties for late submission are quoted from the latest University policy on submission of work for summative assessment: ”The mark awarded will reduce by 10% of the maximum amount available per 24 hours (e.g. if the work is marked out of 100, this means a deduction of 10 marks per 24 hours late. If the work is marked out of 20, the deduction would be 2 marks each 24 hours late.) The penalty applies as soon as an assignment is late; a 10% deduction would be issued if an assignment is submitted immediately after the deadline, and the work would continue to attract further penalties for each subsequent 24 hours the work was late, until the assignment is submitted or no marks remain.” Your submitted solutions should all be in one typed document. This should be prepared using LaTeX or R Markdown. For each question you should provide explanations as to how you com- pleted what is required, show your workings and also comment on computational results, where applicable. When you include a plot, be sure to give it a title and label the axes correctly. When you have written or used R code to answer any of the parts, then you should list this R code after the particular written answer to which it applies. This may be the R code for a function you have written and/or code you have used to produce numerical results, plots and tables. R code should also be clearly annotated. Do not use screenshots of R code/output. Instead, to include R code, use the verbatim environ- ment. Your file should be submitted through the module site on Blackboard to the Turnitin assessment under Assesment & Feedback entitled ’GLM’s Coursework (2025)’ by the above time and date. The work will be marked anonymously on Blackboard so please ensure that your filename is clear, but that it does not contain your name and student id number. Similarly, do not include your name and id number in the document itself. Turnitin will generate a similarity report for your submitted document and indicate matches to other sources, including billions of internet documents (both live and archived), a subscription repository of periodicals, journals and publications, as well as submissions from other students. Please ensure that the document you upload represents your own work and is written in your own words. The Turnitin report will be available for you to see shortly after the due date. This coursework should hopefully help to reinforce some of the methodology you have been study- ing, as well as the skills in R you have been developing in the module. Correct interpretation and meaningful discussion of the results (i.e. attempt to put the results into context) are important in order to achieve a high mark for the coursework. 1. Suppose that we have a random sample of data pairs (x1 , y1 ), . . . , (xn , yn ), where theyi’s are observed values of a response variable, Y , and the xi’s are associated observed values of a numerical covariate. The hypothesised GLM for the data is: yi jxi = µi + ∈i with Yi jxi ~ N (µi , σ 2 ) and E[∈i] = 0, i = 1, . . . , n and 1/µi = ηi = β0 + β1 xi = xiT β, , i = 1, . . . , n You are reminded that it is shown in Section 1.3 of the notes that the Normal distribution belongs to the exponential family of distributions (EFD). The natural parameter,θ, disper- sion parameter, φ, and the functions a(φ), b(θ), c(y, φ) appearing in the definition of the EFD pdf are all clear from there. (i) Give expressions for the vector of partial derivatives ∂β/∂e and the normal equations which are solved to find the maximum likelihood estimates of the parameters in the above GLM. You should specifically define the weights, working responses, and any function of the dispersion parameter which appear in these equations. What is the Fisher Information matrix? Give its particular form for the given GLM. [4] (ii) Give the expression for the log-likelihood function of the parameters based on the random sample (x1 , y1 ), . . . , (xn , yn ). Find an expression for the estimated values of µi i = 1, ..., n for the saturated model, and hence find an expression for the scaled deviance of the model with linear predictors ηi = β0 + β1 xi , i = 1, . . . , n. What is the approximate distribution of the scaled deviance here? [3] A random sample of data such as described above with n = 300 is contained in the file cw-q1-dat . txt. As a preliminary to the next parts, read the data into R, print out the first six rows as a check, and then produce a scatter plot of the data. Please do do not include this plot in your report. (iii) Fit the above GLM to the data using R. Produce a summary of the fitted model object commenting on all the results included. [4] [To visualise your fitted regression model, superimpose the regression curve from your fitted model on to your scatter plot of the data as a check of the fit. However, you should not submit this plot as part of your report.] (iv) Explaining the method you use, calculate an approximate 95% confidence interval for E[Yj x = 0.5]. [Note that the covariance matrix for the parameter estimates can be obtained by using the vcov() function on the fitted model object.] [4] [Total marks for Q1 = 15] 2. The data in this question are from a study in the Philippines looking at household sizes and which factors may be important in influencing them. The data is stored in the spreadsheet called PHH-data . csv which contains n = 1300 observations of households collected from five particular regions in the country. The values of the following variables are included: total = the number of people in the household other than the head of the household. (This is our response variable.) location = where the house is located (Central Luzon, Davao Region, Ilocos Region, Metro Manila, or Visayas) age = the age of the head of household numLT5 = the number in the household under 5 years of age roof = the type of roof in the household (either Predominantly Light/Salvaged Mate- rial, or Predominantly Strong Material, where stronger material can sometimes be used as a proxy for greater wealth). These are coded as ”PL/SM” and ”PSM”, respectively Note that the column in the spreadsheet labelled household is just an index from 1 : 1300. We will be modelling the data using Poisson GLMs with the canonical (log) link function and having the counts in total as the response variable for each one. You can read the data into R and convert the variables location and roof to factor variables with the given reference levels using the following code. phh
Business Analytics (MIS3003S) WELCOME MESSAGE As co-ordinator of the Business Analytics module, I wish to welcome you to the module. To operate effectively within the Global Business World, an understanding of the theory and practice of Business Analytics is essential. This module is designed to deepen your interest and expertise in the area of Business Analytics and the Study Guide is designed to support your learning. While much of the focus is on knowledge acquisition, attention is also given to enhancing and developing your professional and personal skills and competencies. To successfully complete this module, several learning activities are to be completed, which should facilitate the attainment of the module learning outcomes. In recent years, especially with the advent of high-performance computing, analytical and mathematical approaches have become increasingly important in addressing business, engineering and other problems; notably problems where a decision must be made subject to constraints such as limitations on resources, or data analytics problems where we seek useful information in a large dataset. Business analytics is the field of study concerned with quantitative methods of analysis in the context of business. Business analytics means using data, mathematical modelling, statistics, and computer techniques to achieve these goals in the context of business: understanding, prediction, and decision-making. In this module we focus on linear and non-linear modelling, and optimisation techniques. The objective of this course is to develop an understanding of the application of quantitative analytical techniques for problem solving in business and management. Participants will learn how to conceptualise complex business problems and transform. them into a set of equations (models) that describe the problem. For example a regression model, when we are given several data samples (such as transactions) and generate a corresponding mathematical equation, may be used to understand the relationship between the driving variables behind the response, predict response values for unseen scenarios, and make decisions to optimise business profit as a result. We emphasise how to correctly formulate a mathematical model (given a real-world problem description), and the trade-offs necessary between comprehensiveness and usability. Once modelled, the problems can be solved using optimisation techniques such as Linear Regression or Linear Programming. Participants will be introduced to the use of computer packages to implement models for sample problems and assignments. The principles of Active Learning guide the face-to-face contact sessions with students engaging in hands-on mathematical modelling exercises. PART 1: INTRODUCTION This Study Guide is designed to provide you with details of this module, the learning outcomes, delivery and assessment arrangements. The Study Guide consists of 6 parts. Part 1 provides background details to the subject area and the broad aims of the module are set out. Part 2 consists of the module outline. In this part (a) the module learning outcomes, (b) the themes and topics to be explored and (c) the learning supports to be used are explained. Part 3 gives details of the module delivery arrangements. It sets out the session arrangements and the expectations in relation to your prior preparation and student engagement. Part 4 provides details of the assessment techniques used in this module explaining the assessment components and their rationale. Part 5 explains the UCD grading policy and grade descriptors drawing on the university document for each assessment component (i) Assignment 1, (ii) Assignment 2 and (iii) Examination (closed book). Part 6 presents the concluding comments. Brightspace Contents This module will be delivered using face-to-face lectures, with online introduction and conclusion sessions, via UCD’s Brightspace platform. It will consist of a series of chapters. All the contents related to each chapter will be made available through the corresponding Brightspace module page. You will find all the relevant materials under “My Learning” -> “Learning Materials”. Each chapter will have the following documents available to you: · Chapter Notes. This is a single PDF document, with detailed notes related to this chapter. The notes go into much detail, to ensure your understanding of the materials. They have been specifically designed for this course delivery. · Video Lecture. A pre-recorded video lecture, going through the chapter materials. The video lecture follows the notes structure carefully. It explains the concepts in a simple to understand way, going through a slide deck with the lecturer’s voice-over. When required, use of Microsoft Excel and/or other software will be made, to further enhance your understanding. · Slides. A PDF document containing the slides used during the video lecture. These are a compact version of the chapter notes, and are also great later on to recall the concepts learned. · Support Documents. Usually each of the chapters goes through an example scenario, where you learn how to apply an analytics technique. The data files (usually Excel files) related to this, which are used during the video lectures, will be made available. · Practical Excel Implementation Video. The Video Lecture mentioned above carefully goes through the theory of the chapter, and only briefly mentions/exemplifies how to implement it in Excel. As such, one or two tutorial videos go through the Excel implementation of the techniques of the chapter in a lot of detail, to ensure your full understanding. · Practical Exercise Sheet. For each chapter, a practical exercise sheet is available. This is a single PDF document, with either theoretical or practical (Excel) exercises to test your understanding of the materials. · Practical Exercise Support Documents. When required, a set of document related to the exercise are made available (e.g. Excel files). · Exercise Solution. For each exercise sheet, a detailed set of solutions is available. This is usually a PDF document, but may also be an Excel document with the implementation of the exercises. Although the above documents have been carefully designed to ensure your understanding, it is normal to have questions/doubts after the delivery. As such, I will be contactable through e-mail, and will answer any query within at most 48h (usually much less). Background Details Mathematical Programming and other mathematical modelling techniques have long been used in business to model and solve business problems. In recent years, especially with the advent of high-performance computing, mathematical approaches have become increasingly important in addressing business, engineering and other problems; notably decision problems where a decision must be made subject to constraints such as limitations on resources. In the last decade, the discipline of Analytics (incorporating Management Science and Operations Research) has arisen, where mathematical, statistical and computational techniques are used to aid decision making by extracting the crucial information from large datasets. Analytics covers everything from the soft science of multi-criteria decision making to exact approaches used by numerical methods and optimization techniques. Recent surveys by Accenture, IBM and others have shown: · 83 percent of respondents identified business analytics as a top priority and a way to enhance competitiveness; · 72 percent are working to increase their company’s analytics usage; · only half believe they are spending enough on analytics; · over a third said they face a shortage of analytical talent. This course introduces the concept of Mathematical Modelling in Management Decision Problems, and surveys some of the major mathematical models used in such approaches. It concentrates on methods such as Linear Regression, Linear Programming (LP) and its relatives, classification, and clustering. We emphasise how to correctly formulate a model (given a real-world problem description), and the trade-offs necessary between comprehensiveness and usability. You will be introduced to the use of computer packages in working out examples and assignments. Our course will have a practical element, so we will learn to solve problems both on paper and by using computer software. Module Aims The aim of this module is to provide students with an overview of the theory and practice of a range of Mathematical Modelling approaches in Management Decision Problems. This includes: · Describing the main principles of a suite of key regression, linear programming, classification, clustering, and other mathematical modelling approaches as they apply to decision problems and optimisation; · Application of these principles to improve the quality of analysis and decision-making; · Discussion of a portfolio of important business and other applications of these principles; · Understanding the use of mathematical computer packages and information technology as an aid in decision making. These are fleshed out in the themes below. The assessment tasks for this module have been designed with this in mind as detailed later in the study guide. Programme Goals Programme Goals On successful completion of the programme students should be able to: MIS3003S Business Analytics 1) Programme Goal 1: Informed Thinkers: Our graduates will be knowledgeable on management theory and will be able to apply this theory to business problems (Knowledge). Programme Learning Outcome 1a: Explain current theoretical underpinnings of business and the management of organisations. Programme Learning Outcome 1b: Apply appropriate methods, tools and techniques for identifying, analysing and resolving business problems within functional and across functional business areas. Knowledge of analytics methods to apply as aid to resolving business problems Programme Learning Outcome 1c: Demonstrate management skills and leadership skills during a collaborative team based assessment. Team-based project requiring cooperation and leadership skills 2) Programme Goal 2: Communication, Analytical and Critical Thinking Skills: Our graduates will have well developed skills of communication, analysis and critical thinking (Skills and Competencies). Programme Learning Outcome 2a Prepare a short business presentation (written and/or oral) on a current business issue. Project applied to current business issue Programme Learning Outcome 2b: Analyse specific business case studies or problems and formulate a report detailing the issues and recommended actions. Project requiring report on business case and recommended solutions Programme Learning Outcome 2c: Conduct secondary research on management-related issues and report on the findings and draw appropriate conclusions. 3) Programme Goal 3: Personal and Professional Development: Our graduates will demonstrate a commitment to personal and professional excellence and development (Skills, Competencies and Attitudes). Programme Learning Outcome 3a: Develop collaborative learning and team-work skills by engaging in module-related team activities. Team-based project requiring intense collaboration Programme Learning Outcome 3b: Demonstrate capacity for problem solving collaboratively and individually. Individual and collaborative aspects in assessment component 4) Programme Goal 4: Ethical Awareness: Our graduates will demonstrate an awareness of ethical issues in business and their impact on society (Attitudes). Programme Learning Outcome 4a: Demonstrate an awareness of ethical values and business issues concerning the advancement of the broader societal ‘good’. Gain understanding on clarity of result reporting and associated ethical issues Programme Learning Outcome 4b: Illustrate an understanding of how business decisions might influence society and the wider community at large. Understanding of impact of implementing analytics-based decisions
CSC646-B - Introduction to Machine Learning with Applications Part-1: Basic Concepts 1. Cross-entropy loss for classification (18 points) Notations for (1) and (2): xn isan input data sample, and it is a vector. yn is the ground-truth class label of xn. ̂(y)n is the output “soft-label”/confidence of a logistic regression classifier given the input xn The number of classes is K (1: 1 point) Assume there are only two classes (K=2): class-0, class-1, and the data point xn is in class-1 (yn = 1). Assume the output is ̂(y)n = 0.9 from a binary logistic regression classifier. Compute the binary cross-entropy loss associated with the single data sample xn. note: show the steps of your calculations. You will get zero point if only a number is shown. (2: 1 point) Assume there are three classes (K=3): class-0, class-1 and class-2, and the data point xn is in class-2 (yn = 2). Assume the output is ̂(y)n = [0.01, 0.09, 0.9]T from a multi-class logistic regression classifier. Do one-hot- encoding on yn , and then Compute the cross-entropy loss associated with the single data sample xn. note: show the steps of your calculations. You will get zero point if only a number is shown. Show that the function is convex in x, where log is the natural log . Here is a plot of the function, and it seems that the function is convex. Hint: show that then it is convex. Note: show the steps of your calculations (4: 2 points) Explain why cross entropy loss is convex with respect to the parameters of a logistic regression classifier. Note: a few bullet points are just fine, and you may use anything in the lecture notes. (5: 5 points) Let L be the cross entropy loss of a logistic regression classifier for binary-class classification, and let ̂(y) be the scalar output of the classifier for an input sample x. and z = W Tx + b. Compute the derivative note: show the steps of your calculations. You will get zero point if only the result is shown. (6: 7 points) Let L be the cross entropy loss of a logistic regression classifier for multi-class classification, and let̂(y) be the vector output of the classifier for an input sample x. ̂(y) = softmax(z) , where z = [z1, … , zk ]T and zk = Wk(T)x + bk . Compute the derivative which is a vector. note: show the steps of your calculations. You will get zero point if only the result is shown. Note: this results of (5) and (6) are very useful when we apply the cross entropy loss to neural networks. 2. Regression with multiple outputs (3 points) xn isan input data sample, and it is a vector. yn is the ground-truth . yn is a vector and it has two elements [yn, 1 , yn,2]. For example, yn, 1 is income, and yn,2 is age. ̂(y)n is the output of a regressor (e.g., linear regressor) given the input xn . yn is a vector and it has two elements [̂(y)n, 1 ,̂(y)n,2]. There are N data points. (1: 1 point) write down the formula of MSE loss, using yn, 1 , yn,2 ,̂(y)n, 1 ,̂(y)n,2 , and N, where n is from 1 to N (2: 1 point) write down the formula of MAE loss, using yn, 1 , yn,2 ,̂(y)n, 1 ,̂(y)n,2 , and N, where n is from 1 toN (3: 1 point) write down the formula of MAPE loss, using yn, 1 , yn,2 ,̂(y)n, 1 ,̂(y)n,2 , and N, where n is from 1 toN Note: the loss in (1)/(2)/(3) is the total loss calculated using all of the samples (nis from 1 toN) 3. Decision Tree (5 points) A decision tree is a partition of the input space. Every leaf node of the tree corresponds to a region of the final partition of the input space. (1)~(5) are related to the tree below: (1: 1 point) What is the total number of training samples according to the above tree for classification? (2: 1 point) What is the max-depth of the tree? (3: 1 point) What is the entropy on Node-0? (using log base 2) (4: 1 point) How many ‘pure’ nodes (entropy =0) does this tree have? (5: 1 point) How many leaf/terminal nodes does this tree have? 4. Bagging and Random Forest (2 points) (1: 1 point) Bagging will NOT work under a condition: what is this condition? (2: 1 point) The trees in arandom-forest is only weakly correlated in theory: why? 5. Boosting (2 points) What is the difference between boosting (e.g., XGBoost) and bagging (e.g., Random-forest) from the perspective of variance and bias? 6. Stacking (2 points) (1: 1 point) Could it be useful to stack many polynomial models of the same degree? (2: 1 point) Could it be useful to stack models of different types/structures? 7. Overfitting and Underfitting ( 2 points) It is easy to understand Overfitting and Underfitting, but it is hard to detect them. Consider two scenarios in a classification task: (1) the training accuracy is 100% and the testing accuracy is 50% (2) the training accuracy is 80% and the testing accuracy is 70% In which scenario is overfitting likely present? (1 point) Consider two new scenarios in a classification task: (1) the training accuracy is 80% and the testing accuracy is 70% (2) the training accuracy is 50% and the testing accuracy is 50% In which scenario is underfitting likely present? (1 point) Keep in mind that, in real applications, the numbers in different scenarios maybe very similar. We can always increase model complexity to avoid underfitting. We need to find the model with the “right” complexity (i.e., the best hyper-parameters) to reduce overfitting if possible. 8. Training, Validation, and Testing for Classification and Regression ( 3 points) (1: 1 point) What are hyper-parameters of a model? Give some examples. (2: 1 point) Why do we need a validation set? Why don't we just find the optimal hyper-parameters of a model on the training set? e.g., find the model that performs the best on the training set. (3: 1 point) Why don't we optimize the optimal hyper-parameters of a model using the testing set? Terminologies: training(train) set(dataset), testing(test) set(dataset), validation (val) set(dataset) 9. SVM (3 points) (1: 1 point) Why maximizing the margin in the input space will improve classifier robustness against noises? (2: 1 point) Will the margin in the original input space be maximized by a nonlinear SVM? (3: 1 point) What is the purpose of using a kernel function in a nonlinear SVM? 10. Handle class-imbalance for classification tasks (2 points) We have a class-imbalanced dataset, and the task is to build a classifier on this dataset. From the perspective of PDF, there are two types/scenarios of class-imbalance (see lecture notes). Now, assume we are in scenario-1. (1: 1 point) Why do we use weighted-accuracy (a.k.a. balanced-accuracy) to measure the performance of a classifier? i.e., What is the problem of the standard accuracy? (2: 1 point) When class-weight is not an option for a classifier, what other options do we have to handle class- imbalance? 11. Handle data-imbalance for regression tasks (2 points) For regression tasks, is there an issue similar to class-imbalance ? If so, describe the issue and list some possible methods to handle this issue. (read http://dir.csail.mit.edu/) 12. Entropy (6 points) The PMF for a discrete random variable X is [p1, p2, p3, … pk ] where ∑k pk = 1 and 0 ≤ pk ≤ 1 Write down the entropy and prove that: (1: 1 point) entropy is non-negative (2: 5 points) entropy reaches the maximum when the PMF isa uniform distribution, i.e., pk = 1/k Hint: you can use Jensen's inequality or Lagrange Multiplier 13. KL Divergence for probability distributions of discrete random variables. (5 points) There are two probability distributions for the same discrete random variable X: Distribution P: [p1, p2, p3, … pk ] where ∑k pk = 1 and 0 ≤ pk ≤ 1 Distribution Q: [q1, q2, q3, … qk ] where ∑kqk = 1 and 0 ≤ qk ≤ 1 The KL Divergence measures the difference between P and Q, and it is defined as k (1: 2 points) prove that the KL Divergence is non-negative Hint: you can use Jensen's inequality (2: 3 points) show that the KL Divergence is equivalent to cross-entropy when the distribution P is known Hint: read lecture notes Part-2: Programming on classification and regression Read the instructions in H3P2T1.ipynb, H3P2T2.ipynb, H3P2T3.ipynb Grading: (points for each question/task) 11818Question 422Question522Question622Question722Question833Question933Question1022Question1122Question12Bonuspoints (5 )H3P2T12525H3P2T22115H3P2T31010
EMS704: Simulation and Model-Based Systems Engineering Coursework 1: Group Report and Presentation on Simulation Approaches 1 Outline Coursework 1 weighting: 30% of total grade Coursework 1 release date: Monday, 27th January (week 1) Coursework 1 submission format: Group report and presentations (read briefing at QM+) Coursework 1 report due date: Tuesday, 11th March 23:59 (week 6) Coursework 1 presentation date: Friday, 14th March (week 8) Coursework 1 group allocation: You will be allocated a random group with 3 to 5 students on Monday 27th January EMS704 Coursework 1 focuses on the application of simulation approaches taught in Weeks 1–6 to design, build, and validate a simulation model of a real-world system. Students will demonstrate their understanding of various simulation paradigms (discrete, continuous, stochastic, agent-based) and apply relevant tools (e.g., Python, MATLAB, Simulink, NetLogo). The objective is to engage in a full simulation modelling process, including: • Problem definition and requirements specification • Selection and justification of the simulation approach • Simulation model building and analysis • Presentation of outcomes and critical insights 2 Coursework briefing The coursework involves creating a simulation model for a system selected (not limited) from a provided list in Section 3. Each group will perform. the following tasks: Problem Definition and Objectives Students must clearly define the problem their simulation model will address. This involves: • Identifying the system of interest and providing an overview of its context, importance, and purpose; • Outlining the key functionalities and challenges associated with the system; • Defining specific objectives for the simulation, including the goals the model is expected to achieve (e.g., performance evaluation, optimisation, decision support); • Including a visual representation of the system (e.g., diagram, flowchart) to enhance understanding. This could highlight the system's boundaries, major components, or processes; • Explicitly stating any assumptions made during problem formulation. Simulation Approach Students need to select and justify the simulation approaches(s) used in building their model. This process should demonstrate a clear understanding of how the chosen approaches align with the system’s objectives and characteristics. Mixed approaches could be considred when appropriate, as many real-world systems benefit from a combination of simulation approaches to capture their complexities. Key elements to address include: • Choice of Approache(s): Clearly identify the simulation paradigms selected for the model. These could include, but are not limited to: discrete-event simulation, Monte-Carlo simulation, agent-based modelling, bayesian networks. Consider mixed approaches when necessary. For example, combining agent-based modelling with Monte Carlo simulation allows for capturing both individual agent behaviours and system-wide uncertainties. • Justification: Justify the selection of paradigm(s) and tools based on system characteristics. Explain how the approach fits the system’s complexity, dynamics, data availability, and modelling objectives. • Assumptions and Limitations: Discuss assumptions made during the selection process and potential limitations of the approach. Highlight how these may affect model accuracy or scope. • Trade-offs: Identify trade-offs between model fidelity, computational efficiency, scalability, and data requirements. Justify how the chosen approach balances these considerations. Model Design and Implementation Students must develop a conceptual model of the system and implement it using simulation tools. This involves: • Conceptual Model Development: Create diagrams such as flowcharts, block diagrams, or pseudo-code representations to communicate the design process; define the key components, parameters, and processes in the model; describe the relationships between components and how they interact within the system. • Implementation: Implement the conceptual model using at least one simulation tool (e.g., Python, Simulink, NetLogo); provide details on the steps taken during implementation, including setting up input parameters, defining outputs, and coding workflows if applicable. • Integration: Highlight how various components were integrated into the simulation environment; if applicable, explain the handling of multi-domain aspects or interfaces between different paradigms in mixed approaches. Verification, Validation, and Analysis Students must ensure the accuracy and reliability of their model and derive meaningful insights from simulation results. This involves: • Verification: Demonstrate that the model functions as intended and adheres to its design specifications; include methods such as debugging, reviewing the logic of implemented code, and testing individual components. • Validation: Confirm that the model represents the real-world system accurately; compare simulation results with empirical data, theoretical predictions, or expert knowledge; conduct sensitivity analyses to evaluate the model’s robustness against variations in inputs. • Analysis of Results: Present results using appropriate visuals, such as graphs, tables, or charts; interpret findings, identify trends or patterns, and explain their implications for system behaviour or decision-making. • Insights and Recommendations: Provide insights drawn from the analysis and suggest possible improvements or optimisations for the system; discuss any limitations in the experimental process and how they may affect conclusions. Report and Presentation Students must document their work in a professional report and deliver a concise presentation. This includes: • Report: Prepare a detailed report that summarises the entire process, including problem definition, approach, design, results, and insights; ensure the report is well-structured, clear, and visually appealing, with appropriate use of headings, diagrams, and references; Submit a compressed document of the simulation and modelling source code via QM+ with the report. The report should be limited to a maximum of 20 pages, excluding references and appendices. It is recommended to organise the report as follows: o Executive Summary: The report starts with an executive summary on the cover page, which includes the names of group members and provides an overview of the problem, the approaches taken, key findings, and recommendations. o Problem Definition and Objectives: This section defines the problem, outlines the system’s purpose, and specifies the simulation objectives with assumptions and visuals. o Simulation Approach: This section describes the chosen simulation approaches, justifies their selection, and discusses assumptions, limitations, and trade-offs. o Model Design and Implementation: The model design and implementation section explains the conceptual model, its components, and the simulation tool used. o Verification, Validation, and Analysis: This section covers the methods used to verify and validate the model and presents key findings from the analysis. o Conclusions and Recommendations: The conclusions and recommendations summarise the findings and suggest improvements for the system. o References and Appendix • Presentation: In Week 8, each group will deliver a 15-minute presentation highlighting the key aspects of their project, including findings and recommendations, and should be prepared to answer questions from peers and instructors. Additionally, each group will give a 10-minute mock presentation in either Week 5 or Week 6 to outline their progress on the coursework. Note the mock presentations are formative, aimed at providing feedback, and will not be graded. 3 Suggested systems for coursework • Infrastructure systems (e.g., Hyperloop system, HS2 project) • Automotive systems (e.g., electric cars, Formula 1 cars, hybrid cars) • Space systems (Columbia space shuttle, Europa Clipper Mission, James Webb Telescope) • Robotic systems (e.g., an articulated robot) • Healthcare systems (e.g., medical equipment, pharmaceutical systems) • Smart cities (e.g., transportation systems, IoT)
Module code and Title DTS208TC Data Analytics and Visualisation School Title School of AI and Advanced Computing Assignment Title Coursework 1 Submission Deadline 27/Mar/2025 Final Word Count N/A T1 Data Preprocessing (20 marks) Code Result T2. Exploratory Data Analysis (EDA) (25 marks) T2-1: Load the CSV file; show the dimensionality, structure and summary of the dataset. Code Result T2-2: Calculate the number of students whose attendance is lower than 80. Code Result T2-3: Visualize the distribution of previous_scores. Code Result T2-4: Calculate and visualize the number of students with different family incomes. Codes Result Visualization T2-5: Calculate and visualize the average Exam_Score of students corresponding to different Sleep_Hours. Codes Result Visualization T2-6: Analyse data visualization results of T2-5 and summarize your findings in the report. Analysis T3. Modelling (35 marks) T3-1: Create a new column named ‘level’ with values 0, 1, and 2 Code Result T3-2: Choose 5 factors (with nomalization) and apply 1 data analytics method (e.g., kNN, logistic regression, decision tree, random forest, SVM, etc.) to predict the level value. The method you choose The factors you choose Code Result T3-3: Use k-fold cross validation with k = 5 folds to evaluate performance. Code Result T3-4: Select features (factors) and/or tune model parameters to achieve the optimal performance. Show (or plot) model performance under different feature selection and/or parameter tuning settings. Code Result T3-5: Report the best prediction results (i.e., Accuracy, Precision, Recall, F1-score) and the corresponding running time. Code Result T4. Evaluation and Discussion – (20 marks) T4-1: Use one example from the given dataset and draw plots or figures to explain how the input is processed by you model to generate prediction results. Example Figure Explanation T4-2: Discuss the advantages and disadvantages of the model you choose and point out some future directions to further improve model performance. Advantages Disadvantages Future Directions
Module code and Title DTS208TC Data Analytics and Visualisation School Title School of AI and Advanced Computing Assignment Title Coursework 2 Submission Deadline 03/Apr/2025 Final Word Count N/A Note: Please upload the corresponding Python code screenshots for the codes section. T1 Nationwide Visualisation of Air Quality (45 marks) T1-1: Plot the trends of Max AQI for all states from 2000 to 2022. Codes Visualization results T1-2: Create a choropleth map showing the distribution of Max AQI by state for year 2022. Codes Visualization results T1-3: Create a visualization showing the distribution of air quality days (Good Days, Moderate Days, Unhealthy Days, Very Unhealthy Days and Hazardous Days) in California for the year 2000. Codes Visualization results T1-4: Please use the below form. to describe the design of T1-1, T1-2 and T1-3. T1-1 T1-2 T1-3 Mark Channel (Do not just list channels. Please describe the design of them.) Limitation T2. Predictive Analysis for California (55 marks) T2-1: Create 5 data visualisation results to show the relationships between California’s Median AQI and its influencing factors (Year (2000 - 2021), Pop_Est, Good Days, Moderate Days, Unhealthy Days). Codes Visualization results T2-2: Based on the visualisation results, describe the relationship between these influencing factors and the Median AQI. Using these relationships and the 2022 influencing factor data for California, predict the Median AQI for California in 2022 without relying on model training. Justify the reason of your prediction. Year Pop_Est Good Days Moderate Days Unhealthy Days Relationship Prediction Reason T2-3: Train a regression model using California’s data from 2000 to 2021. The model should aim to learn the relationships between Median AQI (target variable) and its influencing factors (Year, Pop_Est, Good Days, Moderate Days, Unhealthy Days). Choose 2 evaluation metrics to evaluate your model and discuss the result. Codes Evaluation results Discuss T2-4: Predict California’s Median AQI for 2022 using the trained model. Codes Prediction T2-5: Compare the results of the visual prediction from T2-2 and the model-based prediction from T2-4. Discuss the differences and explain which approach you find more reliable and why. • Comparison and Discussion Comparison and Discussion
ARE 132: Cooperative Business Enterprises Final Project: Case Study of Cooperative Business Enterprises Learning objectives: This project allows you to apply the material covered throughout the quarter in an analysis of an existing cooperative business or organization of your choice. An important aspect of successfully completing this project is to identify a business issue or market failure and clearly focus on offering possible solutions. The more specific you are, the easier it will be to complete your project successfully. Throughout your analysis, you are expected to develop an in-depth understanding of the business or organization and think about potential strategies to address your identified issue. I strongly encourage you to reach out to the business or organization you are analyzing and talk to management, members and/or patrons. Content and Structure: Your final project should be no longer than 10 pages (double-spaced), and should provide an answer to each of the following seven questions: 1) What is an issue the business or institution is currently facing? 2) Description of the cooperative (collective or organization). a) Industry of operation b) Products and/or services provided c) Organizational structure (e.g., who is horizontally coordinating and are they vertically integrating?) 3) What economic conditions (production, market, regulatory) are affecting the cooperative (either its formation or its current operations)? 4) What economic rationale supports the cooperative’s existence (more than one rationale may be relevant)? 5) How are contemporary cooperative principles demonstrated? (At the minimum, address the three principles defined by the USDA, but you might also refer to the seven principles defined by ICA depending on the business.)? 6) Has the firm’s cooperative structure (either organizational principles or government regulations) had an adverse impact on the cooperative’s business performance? What are specific management challenges faced and market strategies pursued? 7) What specific recommendations and suggestions can you give based on your analysis? Sample Outlines: Please note that you do not have to follow the structure suggested here and that your headings could either be based on structure or content. Executive Summary The Business Issue Description of the Co-op Application of Contemporary Principles Economic Rationale and Insights from the Literature Business Performance and Challenges Faced Recommended Strategies References or (less detailed but similar content) Executive Summary The Business Issue Analysis Recommendations References We will provide additional guidance and feedback throughout the quarter. Please also ask questions in lecture and reach out during student hours. Groups: You will be working on this project in groups of 5-7 students. Groups will be assigned during the second week of instructions. While I encourage you to assign tasks to each other once you are assigned to groups, each member should be familiar with and contribute to all aspects of this project. That includes reading and editing drafts before submissions. Deliverables: You are asked to submit a one-page case study proposal (topic and brief outline) by the end of the third week (Sunday, January 26th) to receive initial feedback. Your complete case study is due at the beginning of our last lecture (Thursday, March 13). While you have to upload a pdf version to Canvas, you can also bring a printed version to class that day. A grading rubric and further instructions are posted on Canvas. Please note that all group members will receive the same grade for the final project. Please reach out if you experience issues in terms of group participation. If a member has not participated until week 8 of the quarter (Tuesday, February 25) he/she/they can be removed from the group and will have to complete their own project. A 5-point penalty will be applied to individually submitted projects unless an exception or special permission was granted. Please note that the final project assignment created on Canvas includes a rubric for additional guidance regarding grading guidelines. We do not prohibit use of GenAI (e.g., ChatGPT and Gemini), but encourage you to be thoughtful and transparent. When carefully checked for accuracy, these tools can enhance your research and scientific writing, but overreliance on AI has been shown to hinder critical reflection and erode expertise. These tools are trained to find patterns and generate text by predicting the likeliest response based on their training data. They do not understand context or create original ideas, and they make a lot of mistakes. While they can help you brainstorm topics and provide basic editorial feedback, they are not a substitute to reviewing the literature yourself, critically reflecting on and continuously editing your writing. For guidance on how to cite your AI use, please see this link. If you are looking to improve your writing, I also encourage you to take advantage of the services offered by the AATC Writing Support Center. References: You are free to use academic resources, reports, and websites. Please do not just copy and paste information and be sure to reference all your sources. You are required to cite a reference even if you rephrase what you have read. That is also true for graphs or tables that you are including, unless you are creating those yourself. Proper citation: If you are citing an article or other sources in the text, you do not have to write out the title and full author name. Instead, you include the last name and publication year only. If you cite word for word, you will also have to reference the page number as well. You then include the complete information for all of your references used in the reference section at the end of your paper. Citation styles vary slightly across academic outlets and other publications in Economics. I do not require that you follow a particular style. as long as you are consistent throughout and include all information to identify and access your source. Please refer to sample references and styles published by AEA (https://www.aeaweb.org/journals/policies/sample-references) for additional guidance. Cooperative Business Enterprises for your Analysis: You are free to complete your analysis on any cooperative business or organization. Please check the NCB Co-op 100 list (top 100 cooperatives in America) for a wide variety of businesses. Below, I also provide a list of cooperative businesses or related organizations I have worked with in the past: Possible Businesses, Marketing agreements. etc.: 1. Blue Diamond 2. BUCRA (Butte County Rice Growers Association) 3. California Leafy Greens Marketing Agreement (LGMA) 4. Hass Avocado Board (Marketing Order) 5. CoBank 6. Davis Natural Food Co-op 7. Golden State Power 8. Land O'Lakes 9. Full Belly Farm (CSA) 10. LBMX (Purchasing Co-op) 11. Pachamama Coffee (Global Farmer’s Cooperative) 12. Sacramento Natural Food Co-op 13. SunMaid Growers 14. The Cheese Board Collective 15. California Center for Cooperative Development (Non-profit organization supporting a variety of co-ops)
Instructions for Individual Assignment • Please answer the following questions. • No need to include a cover page. But you should include the following information at the beginning of your assignment: o Course Title: Green Supply Chain Management o Course Code: GTSU2003 o Class Number (100X) o Individual Assignment o Student ID o Student name (Pinyin) • The maximum number of assignment pages is no more than six pages (Times New Roman, single-spaced). Please aim to use your own words to answer the questions rather than copying and pasting from sources such as the slides or the textbook. • Please use the APA referencing format when citing others’ works • This assignment is worth 20% of the total course marks • Due date: This assignment is due by 5:00 pm on Friday, March 7, 2025. Please upload a soft copy to iSpace and submit a hard copy to the TA (Teaching Assistant). Late submissions will incur a deduction of up to 20%. If late for more than one week, the marks will be zero. • Turnitin will examine the submitted assignment. If the similarity with a single source exceeds 10% or the total similarity exceeds 50% (which is higher), the assignment will be chosen for further investigation. The grade for cases of serious plagiarism may be reduced by at least 50%. • No AI tools are permitted for this assignment. Multiple Choice Questions (25%) Input the answers for MCQs 1 to 5 in the table below: 1 2 3 4 5 1. Which of the following is NOT about social sustainability in the supply chain? A. Public health. B. Route optimization. C. Animal welfare. D. Production security. 2. Ideally, the preferred inventory strategy to enable a supply chain manager to forecast the market demand to acquire more competitive advantage would be _____ . A. JIT B. Agile C. Lean D. Cross-docking 3. Which one is NOT part of Sustainability? A. Economic dimension B. Governance dimension C. Environmental dimension D. Social dimension 4. Regardless of whether it’s in containers or in bulk, freight is usually a less speedy but economical choice for conveying predominantly low-cost, high-quantity products over land routes or inland areas. A. Sea B. Rail C. Road D. Air 5. Which one is NOT considered as a negative externality of freight transport? A. Increased noise B. Increased concern about public health C. Reduced air pollution D. Increased traffic congestion and accidence Short Answer Question (75%) Questions 1 (25%) Please discuss five of the six key logistics and supply chain management trends that can impact sustainability. For each of the five trends you select, clearly outline the effect and explain the reasons behind the effect. Questions 2 (24%) Please discuss four levels of corporate social responsibility (CSR) with business examples. Questions 3 (26%) Please discuss the differences between logistics and reverse logistics with sample pictures (please draw two graphs to compare logistics and reverse logistics after your discussion).
Digital Banking and Fintech N1623 2024/2025 Post Project: Digital Banking Business Report: A comprehensive analysis of a selected bank’soperations, focusing on strategic aspects, industry trends, and competitive landscape. Utilizing the CAMELS framework, we will conduct a thorough evaluation of the bank's capital adequacy, asset quality, management efficiency, earnings, liquidity, and sensitivity to market risk. Project-Tasks Percentages of Project mark Digital Banking Business Report 80% (200 words) Video 10% (5-10 minutes) Student participation 10% Bank Selection and Consistency: Students are required to select a single bank and competitor for in-depth analysis. This bank and competitor will be the subject of both the business report. This ensures thematic coherence and allows for deeper, more nuanced analysis across both components. The total word count is 2000 word ( +/-10%) excluding Appendix, Figures and Tables. The business report and video should exhibit a clear and logical connection. Ensure that insights from the business report inform and enrich the analysis presented in the video. For example, findings about the selected bank's overall digital strategy in the report could be used to contextualize the specific mobile banking features and embedded technology solutions discussed. The availability of financial data for last five years (balance sheet, income statements, annual reports) from Bloomberg Database. The document must be written in Harvard Style. Competitor Selection: Once you've chosen your primary bank, select a suitable competitor for comparison. Ensure the competitor offers comparable mobile banking services and integrates AI in similar ways. Consider: - Banks of similar size and market segment (RV function, or Fame/Orbis peer analysis or Statista) - Banks recognized for AI solutions for banking operations/ products (Bloomberg Terminal -Function CN, DES, DS) - Banks operating in the same geographical region (Bloomberg Terminal -Function RV, DES) Data Sources To be able to process this report, student need to use Bloomberg Databases or Annual Report Other valuable sources of information: - Read through Directors' report (Bloomberg Terminal -Function CN, DES, FA, DS) - Read through other reports in front of Statutory accounts - Read any articles on the company in the financial press - Web (Google, Bing, FT etc...) search of a company for comments by market analysts - Sussex Online Databases (Orbis, Statista, Fame) - Teaching references and resources. Time management The earlier you start the project, the better. The lectures and seminars are designed to help you work on your project. The best approach would be to meet up weekly and apply the knowledge gained from the classes to advance your assignment. For each seminar, you will have the opportunity to ask questions regarding your project during the last 10 minutes at the end of the workshop. This skill is related to how to create a project schedule. The only way to achieve the project’s goals within the given timeframe that has been decided on is to breakdown the goals into tasks on a timeline. You are required to set up a realistic schedule and then manage the resources needed to keep on track so that the project can be successfully concluded on time. You might use an online Gantt Chart. Risk management Any project is inherent with risk. It is essential to become familiar with these issues before they become problems. Different problems can arise, such as other assignments, lack of access to the database, the crash of computer systems etc... It is essential to keep track of different documents and other vital issues Attend Lectures and Seminars: These sessions will provide crucial details about each task and the expected outcomes. Active participation is essential to ensure a comprehensive understanding of the material. Please do not hesitate to ask clarifying questions during these sessions. Digital Banking Business Report This comprehensive report undertakes a deep dive into the banking operations of chosen bank, assessing its digital strategic direction, performance within industry trends, and competitive landscape. Utilizing the CAMELS framework, we will conduct a thorough evaluation of the bank's capital adequacy, asset quality, management efficiency, earnings, liquidity, and sensitivity to market risk. In this report, students should evaluate the financial and risk position and then look at how the economic situation affected the bank strategy. General points for this report The general points areas follows: - Do not ramble or waffle - One point per sentence - Keep to simple sentences - Check spelling and grammar. Required Format - Header: Bank name - Footer: date, page numbers - Wide margin on the left so report can be easily read when bound/filed - Every student must submit one for selected approved bank Size of this section - Unlikely to be as big as the section on analytical review - Quality needed, not quantity - Marks for showing you understand the implications of financial ratios. Key Areas of Exploration: Strategic Analysis: Examining the bank's banking strategy, including its vision, mission, goals, and key initiatives, especially in the context of digital banking strategy. Competitive Landscape: Identifying and evaluating the main competitor in the banking space. CAMELS Analysis: Employing the CAMELS framework to comprehensively assess the bank's capital adequacy, asset quality, management efficiency, earnings, liquidity, and sensitivity to market risk. Analysing the bank's financial performance in the context of its digital banking operations, focusing on key metrics like user adoption, transaction volume, and revenue generated. Risk Analysis: Analysing the bank's risk e in the context of its banking operation, especially in the context of the usage of technology Digital Banking Strategy: Embracing Embedded Technology and Product Innovation This strategy outlines our commitment to leveraging embedded technology and fostering aculture of innovation to deliver exceptional digital banking experiences. Analyze a specific bank's digital innovations and embodied technologies over the past five years, comparing them to competitors. Base your analysis on the IMF's "Evolution of Finanscape" and their work on major technology transformation services (Lecture and course references). Evaluate how these investments impact the bank's financial ratios, performance, and strategy. Consider the bank's stated digital banking strategy (e.g., "Embracing Embedded Technology and Product Innovation") and analyse its actual implementations and performance against this strategy. Report Aim: This report aims to provide a comprehensive and insightful analysis of banks' operations, offering valuable information for investors, industry professionals, and consumers alike. By understanding the bank's strategic direction, its position within the evolving landscape, and its performance relative to competitors, the report seeks to shed light on its future prospects and potential challenges in the digital banking arena. The report structure. This report is required to have the following sections: 1. Introduction: A description of the selected bank (100 words) 2. Bank performance analysis (600 words) 3. Bank Risk analysis (600 words) 4. Bank strategy and Innovations (600 words) 5. Conclusion (100 words) - The bank to be analysed must be approved by the lecturer before submission. Include the approval email as part of your submission. -Include Bibliography at the end of your report -Include a list of all appendices at the end of your report -The report, including all data, figures, and calculations, should be submitted as Excel files. The report content. Title Page The title page must include: - University of Sussex - Module Name and Module Code - Bank Name - Student candidate number 1. Introduction A summary of any relevant information as follows: Geographical location/s Products/Services (in tables or graphs) Direct competitor (Peer Analysis) Bank structures (for example, Personal banking, Business Banking etc. as in tables or graphs) 2. Bank Performance Analysis Analytical review - Historical review as a comparison to the competitor - Trend analysis of required ratios over the last five years - Define strengths and weaknesses to detect in the bank’s performance - Interpretation of bank position with respect to competitor - Generation using Bloomberg's built-in functions - Summary Table of ratios in Appendix Interpretation of ratios Group into categories - CAMELS rating analysis - Efficiency Ratio Those are the two basic types of analyses - most used, as each can be used for some financial companies. It is the main ratio that students must discuss in their report. 3. Bank Risk Analysis Analytical review - Historical review as a comparison to competitor - Trend analysis of the required ratios over the last five years - Interpretation of bank position with respect to competitor -Define strengths and weaknesses to detect in bank’s risk - Generated using Bloomberg's built-in functions -Summary Table of ratios in Appendix Interpretation of ratios Group into categories: - Overall Risk - Liquidity Risk - Credit Risk - Off-Balance Sheet risk - Capital Risk 3. Bank strategy and Innovations Analyze the digital innovations and embodied technologies implemented by a specific bank over the past five years, comparing them to its competitors. This analysis must be grounded in the frameworks presented in the lecture, specifically referencing the IMF's "Evolution of Finanscape" and the IMF's work on major technology transformation services (as detailed in Lecture 5,6,8,9 and the course reference list). Furthermore, analyze how these investments in new technologies and innovations have impacted the bank's key financial ratios, overall performance, and strategic direction. 4. Conclusion - The conclusion must be drawn from the evidence in your report - Must refer to your workings and your conclusions - All conclusions must be based on facts Bibliography - Must reference all sources - Web pages must include the date of a hit as well as the web address - Press must include the date of publication as well as the writer - References can be presented in Harvard style
CS 1103 Spring 2025 Introductory Programming for Engineers and Scientists EXAM 2 Practice Exam 1 Practice requires you to download the grader program from BrightSpace. It is called e2practice.p and you can find it under "Exam" and then "Exam 2 Practice". Create a new blank folder where you intend to work on this test and copy the p file there. Make sure that it was not renamed in the process to something like e2practice(1).p or similar. When you start MATLAB, set your current folder to this very same folder. Solve each problem in MATLAB. Make sure to name your solutions (that is, your m-files) as instructed below and that they are located in the same folder as the grader. Run the grader in MATLAB by typing the command: e2practice. The program will display a menu where you have the option to test individual problems one by one. To get the score for your test, select to grade "All Problems." The grader will ask for your vunetid. Makes sure you type it in correctly, all lower case and no extra spaces. The grader will display your score. Submission: N/A (for the real test you will need to submit all your m files) Grading: Number of problems solved: points: 1: 40; 2: 70; 3: 85; 4: 95; 5: 100; Password: LoopyTest Problems: 1. Write a function called pe21 that takes two input arguments indicating the current time: hr for hours and min for minutes. The function checks whether the two inputs can in fact represent time according to the international format. That is, it needs to check that they are both positive integer scalars and hr is between 0 and 23 inclusive and min is between 0 and 59 inclusive. The function returns a logical value: true if the input arguments satisfy these rules and false otherwise. 2. Write a function called pe22 that takes a positive integer scalar input argument called k. Make sure to check that k satisfies these assumptions and if it does not, return -1. The function computes and returns a row vector consisting of the first k elements of the sequence s defined as s(1) = 1 s(n) = n*s(n-1) - 1 for all n >= 2 Note that this is not MATLAB code: it is the mathematical definition of the sequence where s(n) means the nth element of the sequence. 3. Write a function called pe23 that takes A, a matrix and returns another matrix with two columns called ind. The rows of the matrix contain the row and column indexes of all the negative elements of A. The row indexes go into the first column, while the column indexes go to the second column. The function provides these indexes according to row major order of A. For example, the call >> negs = pe23([-1 -2 0; -3 2 2]); will make negs equal to [1 1; 1 2; 2 1]. If there are no negative elements in A, the function returns the empty array. 4. Write a function that is defined like this: function w = pe24 (v,a,b,c). The first input argument v is a vector, while a, b, and c are all scalars. You do not need to check them. The function replaces every element of v that is equal to a with b and c. For example, the command >> x = pe24 ([1 2 3],2,4,5); makes x equal to [1 4 5 3]. 5. Write a function called pe25 that takes four scalar positive integer inputs, month1, day1, month2, day2. These represent two days in 2025. It is guaranteed that the first date is no later than the second one. The function returns a positive integer scalar that is equal to the difference between the two dates in days. For example, this call to the function pe25 (1,30,2,1); would return 2. You do NOT have to check that the input values are of the correct types and they represent valid dates. You are not allowed to use the built-in functions datenum or datetime. Here are the number of days in each month from January to December in 2025: 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31.
