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Problem Set 2 Regression and Uncertainty Instructions •Submitboth fileson Canvas:One knitted PDFand original.Rmdfile. –Failure to submit both the knitted PDF and the .Rmd will result in a 20-point deduction. •Your knitted PDF should bereadable on its own. Organize your work using clear section headers, include brief text explanations between code chunks, and display only the outputs needed to answer each question (avoid printing large objects or long intermediate outputs). –Make sure all tables are clearly labeled and easy to read in the final PDF. –For every plot: include a clear title, axis labels (with units if relevant), and readable text. •Include a short“Use of AI”subsection at the end of your submission (even if you used none). –You can use AI tools for debugging error messages, clarifying how an R function works, or checking syntax, etc. Donotuse AI as a substitute for your own understanding—you should be able to explain and justify everything you submit. –Failure to disclose AI usage will result in a 20-point deduction.This exercise is based on the article, Testa, A., Young, J. K., & Mullins, C. (2017). “Does Democracy Enhance or Reduce Lethal Violence? Examining the Role of the Rule of Law”Homicide Studies, Vol. 21, No. 3, pp. 219-239. The paper examines the cross-national causes of homicide rates. Briefly, many scholars have looked at how institutions influences homicide rates, arguing democracy can reduce lethal violence. The authors of the paper claim that all previous claims are incomplete as they do not unpack the concept of democracy and examine different dimensions of the concept such as how having an independent jury impact homicide rates. In short, some democracies may have more homicides than others. Let’s find out why. Here is a description for the variables inhomicide2.dta: Name Description countrycountry names yearyear homi_ratehomicide rate region1the numeric code indicating the…

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Assignment A2 Gradescope rubric General marking principles (all questions) Code and discussions should be pitched at an appropriate level, based on concepts and principles as introduced in the course materials. Marks may be reduced for excessively verbose or overly technical implementations or discussions, or for excessively commented or overly documented code. Marks may be significantly reduced for discussions which do not align with associated code or output. Marks may be reduced for poor scholarship. 1.1 6: Comprehensive comments which clearly and correctly describe all elements of the code. 5: Intermediate mark 4: Comments which describe all or almost all elements of the code, with the majority of key elements described correctly. 3: Intermediate mark 2: Brief comments which describe at least a few elements of the code correctly. 1: At least one relevant comment. 0: Substantially incorrect or incomplete. 1.2 10: A comprehensive investigation of the problem. A clear and complete discussion which includes appropriate mathematical arguments where relevant, is fully supported with numerical evidence, and which draws appropriate conclusions from the numerical evidence. The discussion is supported with appropriate plots/output with appropriate formatting. Code is clear, concise, well formatted, and easy to understand. 9: Intermediate mark 8: Intermediate mark 7: A good investigation of the problem, but the investigation may be more limited in scope. The discussion is generally clear, draws appropriate conclusions, and includes some appropriate mathematical arguments where relevant, but may be lacking in a small number of aspects. A small number of conclusions may be overly strong, or may not be fully supported by numerical evidence. The discussion is supported with appropriate plots/output, which may be lacking in a small number of aspects. Code is generally clear, concise, well formatted, and easy to understand, but may be lacking in a small number of aspects. 6: Intermediate…

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CS-115: Programming 2 Coursework 2 Troy Astarte Due: 2026-04-20 at 14:00 W eighting: 10% of the 15 credit module T otal Marks: 100 Document version: 1.2 Scenario In this coursework, you will build a simulation system for infection disease modelling. The key components are Pathogens—the diseases—and Disease Centres, locations around the country where diseases originate and spread. You will base this on a GUI map of the UK and you will be able to see the pathogens as they move around. Finally, the system will produce traces of pathogen routes. Learning Outcomes This coursework will help you learn: • inheritance hierarchies • polymorphism, with overloading and overriding • file reading and writing • working with data structures • modifying an existing codebase (the provided files) • integrating your work with a remote and opaque codebase (Autograder) Java Version Your code will be tested with Java 17 and I strongly suggest you use Java 17 to write it. Marking, Autograder & Feedback Each task within this coursework has multiple tests associated with it on Autograder. The tests you will be able to see will be worth some marks and provide you with some knowledge that your code is functioning as it should. However, there are hidden tests which will also award marks for code quality and presentation; these are checking your understanding of the more in-depth concepts, such as ensuring you’re using object-oriented principles (e.g. encapsulation) along with following the coding conventions. You will be able to see your scores on these tests, as well as additional feedback on where you might have lost marks once the final mark is published. You must submit your code to Autograder. We would advise you to upload and test your code regularly, at least at the end of each task completion. This will help…

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EIE111 Object-Oriented Programming Course Project: The Wordle Game Perhaps you have heard about the Wordle game: https://www.nytimes.com/games/wordle/index.html The goal is to guess a 5-letter word within at most 6 trials. In this game, the program randomly picks a word as the answer, and the user can input their guess. After submission, the program should check this input and return the result as color text. Each letter is shown in one of the three colors: ➢ Green: The letter appears in the answer and is in the correct place. ➢ Yellow: The letter appears in the answer, but it is in the wrong place. ➢ Gray: The letter does not appear in the answer. The game will finish if the guess is correct or all the 6 trials have been used. In the supplementary files, you will find a list of possible answers and a list of possible guesses. For each run of the program, an answer word is randomly chosen from the first list. Then the user can start the guess. If the user’s input falls in one of these two lists, the program should check its correctness; otherwise, you need to inform the user that this is not a valid word. I provide you with a sample executable file that con tains the basic features of Wordle (note: run it on a windows system): This game could be extended to other languages. Here is a Chinese version: https://xiaoce.fun/xunhualing The file “poem.txt” is a sample dataset of Chinese poems. You may consider choosing a larger dataset: https://github.com/open-chinese/poetry-collection You need to create a console application including these functions and features: 1. Guessing a 5-letter English word. 2. Guessing a sentence of 10 Chinese characters. 3. Guessing a sentence of 14 Chinese characters. 4. Showing characters in different colors according to the…

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MATH 11205: Machine Learning in Python 2025-2026 Project Description We will be using a subset of the data collected by UNICEF, called the Multiple Indicator Cluster Survey (MICS). A simplified version of the data will be used for this project, provided in the file unicef malawi.csv, after some initial cleaning steps (described below). This smaller data set focuses only on data collected in Malawi in the years 2019-2020. The dataset collects a number of indicators on the well-being of children, including childhood depression. Your goal is to build a model to predict if children suffer from feelings of depression. Assignment Goal For the purpose of the project, consider yourself aData Science Consultantwho has been hired by UNICEF to analyse childhood depression in low-income countries. The mental health of the next generation – those aged under 18 years - is a societal priority. Identifying and treating mental health early in life has lifelong impacts on physical health, education, earning potential, relationships, identity formation and life satisfaction. Further, the burden of poor mental health disproportionately falls on lower- and middle-income settings (LMICs), and on women and young people in particular. Mental health is influenced by various factors, at the level of the child, parent, and societal environment. For an integrated model of mental health, an approach that combines these multiple factors together is required, and is possible through the UNICEF’s Multiple Indicator Cluster Survey (MICS), which collects information on the child, parent, and household environment. For further details on the data, please see the main webpage for the Multiple Indicator Cluster Survey (MICS). Towards this aim, you have been asked to use this data to build a classification model to predict if a child suffers from depressive feelings. Depression is a heterogeneous condition, and to the question How often does the child…

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1 Introduction and instructions Time allowed – One week READ THESE CAREFULLY You will be expected to submit a single report and a single R script. Only one person from the group should submit both of these. If multiple members of the same group submit the report, only the latest submission will be considered for marking. This group assessment has six questions. Each group member should pick one of these questions. Please make it clear in the jointly submitted document which question was done by which group member by adding their student ID to the top of the appropriate page. If you left your group and are attempting the assessment of your own, please pick three questions out of the six included below. At least one question will need to come from questions 1-3, and at least one question will need to come from questions 4-6. This is an ‘open book’ examination. You are free to use any written materials you find useful, including your own notes and annotations. You are allowed to use generative AI software, but you must reference it if you do so following UCL guidelines (https://www.ucl.ac.uk/students/exams-and-assessments/assessment-success- guide/engaging-ai-your-education-and-assessment). You can and indeed, should work with other group members to complete the report . As suggested earlier, the introduction, methods, and conclus ions sections will need to be co -authored and assessed as one, whilst the methodological approaches will be graded individually. The introduction should provide a general introduction to the data. The methods section should outline the methodological approaches chosen by the group members as well as justifying how and why certain variables were chosen. The discussion should draw tog ether the results and speak to the p olicy relevance of the findings . Please remain consistent regarding the choice of variables as this is the only…

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COMP2034: Sample Report (partial) Write-up Creating a Blockchain Application for Supply Chain Management in the Wagyu Beef Industry 1 | P a g e Sample Report (partial) write-up Table of Contents Design & Background Research on Real Life Supply Chain Management ............................................... 1 Case Scenario ....................................................................................................................................... 3 Aim of Blockchain ................................................................................................................................ 4 Smart contract (as agreed by every entity involved) ........................................................................... 4 Entity involved ..................................................................................................................................... 4 Supply Chain Flow In Wagyu Beef Industry ......................................................................................... 4 Design Structure of Blockchain & Block ............................................................................................... 6 Design Structure of Transaction ......................................................................................................... 7 Screenshot of output ............................................................................................................................... 8 User interface in main.cpp ................................................................................................................... 8 Print created blockchain ...................................................................................................................... 8 Search block by block ID in blockchain ................................................................................................ 9 Search transaction by ID in blockchain .............................................................................................. 10 Search transaction info by transaction ID and key in blockchain ...................................................... 10 Checking validity of input .................................................................................................................. 10 Feedback Questions ............................................................................................................................... 12 How was your overall experience regarding doing this coursework? ............................................... 12 Key things you’ve learned while doing this coursework?.................................................................. 12 Noteworthy difficulties you faced ..................................................................................................... 12 Reference list ........................................................................................................................................ 13 2 | P a g e Sample Report (partial) write-up Design & Background Research on Real Life Supply Chain Management Case Scenario The designed blockchain is to track the shipment of exclusive Wagyu beef from Farm A to its partnering restaurants. In reality, customers at high-class restaurants are paying the luxury price for exclusive grade A Wagyu Beef. Since customer is paying higher price, it is their right to ensure that the product they receive is not ordinary beef. As a guarantee that customers are getting what they’ve paid for, a Certificate of Authenticity is presented along with the Wagyu Beef as a proof of its high quality to verify important details about the specific raw material they have received/how product is made. Since the Wagyu…

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MAS8600/MAS8505 Project: Learning Analytics James Bentham Autumn 2025 Overview This coursework is worth: • 33% of the overall mark for the 30-credit MAS8600 Graduate Foundations of Statistics and Data Science module. • 100% of the overall mark for the 10-credit MAS8505 Graduate Foundations of Statistics and Data Science (Applications) module. The analysis report is worth 50% of the mark for the assessment, and the ProjectTemplate Directory is worth 50%. You should submit your coursework to Canvas by 4pm on Friday 16th January 2026. If you have any questions regarding the coursework, please ask during a practical session, during office hours, or by emailing james.bentham@ncl.ac.uk. Context Learning Analytics, a rapidly growing application area in Data Science, is defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environment in which it occurs.” Existing mechanisms to record student engagement such as attendance monitoring fail to capture the extent and quality of engagement outside the classroom environment. Further complementary sources of data are routinely collected about our learners, e.g., use of on-campus facilities, Virtual Learning Environment (VLE) and ReCap access, as well as student wellbeing referrals. However, this information currently resides in a number of silos. Learning Analytics seeks to aggregate these sources of data to derive shared insights, and provide effective measures of engagement. Insights may inform learning design, inform intervention processes for at-risk students, and improve student attainment. Task Description We have data from 7 runs of a massive open online course (MOOC) entitled “Cyber Security: Safety at Home, Online, and in Life” developed by Newcastle University and made available to the public by the online skills provider FutureLearn. We have raw data collected by FutureLearn on learners as they progressed through the course, along with some…

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Lab 4: Statistical Tests and Regression 27 February 2025 # Load required package for reading Stata files # Note: The .dta file is a newer version, so we use the haven package library(haven) # Load the data ajr <-read_dta("ajr.dta") # Display basic information about the dataset dim(ajr) [1] 163 17 names(ajr) [1] "shortnam" "africa" "lat_abst" "malfal94" "avexpr" "logpgp95" [7] "logem4" "asia" "yellow" "baseco" "leb95" "imr95" [13] "meantemp" "lt100km" "latabs" "loghjypl" "other" summary(ajr) shortnam africa lat_abst malfal94 Length:163 Min. :0.0000 Min. :0.0000 Min. :0.0000 Class :character 1st Qu.:0.0000 1st Qu.:0.1444 1st Qu.:0.0000 Mode :character Median :0.0000 Median :0.2667 Median :0.0005 Mean :0.3067 Mean :0.2956 Mean :0.2945 3rd Qu.:1.0000 3rd Qu.:0.4469 3rd Qu.:0.7315 Max. :1.0000 Max. :0.7222 Max. :1.0000 NA’s :1 NA’s :6 avexpr logpgp95 logem4 asia Min. : 1.636 Min. : 6.109 Min. :0.9361 Min. :0.0000 1st Qu.: 5.886 1st Qu.: 7.376 1st Qu.:4.2246 1st Qu.:0.0000 Median : 7.045 Median : 8.266 Median :4.4427 Median :0.0000 Mean : 7.066 Mean : 8.303 Mean :4.5960 Mean :0.2577 3rd Qu.: 8.273 3rd Qu.: 9.216 3rd Qu.:5.6101 3rd Qu.:1.0000 Max. :10.000 Max. :10.289 Max. :7.9862 Max. :1.0000 NA’s :42 NA’s :15 NA’s :76 yellow baseco leb95 imr95 meantemp Min. :0.0000 Min. :1 Min. :37.24 Min. : 4.90 Min. :-0.20 1st Qu.:0.0000 1st Qu.:1 1st Qu.:52.25 1st Qu.: 27.98 1st Qu.:21.56 Median :0.0000 Median :1 Median :65.70 Median : 49.45 Median :24.47 Mean :0.4724 Mean :1 Mean :62.08 Mean : 57.07 Mean :23.13 3rd Qu.:1.0000 3rd Qu.:1 3rd Qu.:72.05 3rd Qu.: 81.75 3rd Qu.:26.39 1 Max. :1.0000 Max. :1 Max. :78.98 Max. :170.00 Max. :29.30 NA’s :99 NA’s :103 NA’s :103 NA’s :103 lt100km latabs loghjypl other Min. :0.0000 Min. :0.00000 Min. :-3.5405 Min. :0.00000 1st Qu.:0.0942 1st Qu.:0.08889 1st Qu.:-2.7411 1st Qu.:0.00000 Median :0.2392 Median :0.15000 Median :-1.5606 Median :0.00000 Mean :0.3739 Mean :0.17823…

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Page 1 of 10 RMHI/ARMP Problem Set 1 2026 Word count total: 1200 Hello everyone! This is the description for the assignment, which is due on Canvas on Monday April 13, 2025 by 11:59pm Melbourne time. You’ll need to submit a Word-knitted version of the completed R Markdown file found in this zip file, according to the following instructions: 1. Rename the document called pset1.Rmd as studentID-pset1.Rmd. (Replace studentID with your student ID number). This is your R Markdown file, where you’ll be putting all your code and answers. 2. Replace “Your ID goes here” in the header of the R Markdown file with your student ID. (Keep the quotes or it won’t knit properly.) Do not additionally include your name in the header of the R Markdown file or the filename as we will be marking papers anonymously. 3. While we encourage collaboration in tutorials and learning in general, you should not be collaborating with anybody AT ALL for this assignment. That means no sharing code privately or publicly; even talking in the abstract about problems will effectively be collusion. You should be completing it independently, with no help from any other person in any capacity. Of course, as always, you are free to use any of the resources from the class to help you, and you're also free to google or look anything up that you like (as long as you aren't asking anybody, including discussion boards or AIs, questions related to this assignment). Note that we do look at places like chegg and will follow up if anything from this problem set is posted there. 4. Plagiarism check is enabled and you can check the similarity report on your submission. In previous years we have found people who tried to cheat, so please don’t risk it! That…

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Assignment A2 Gradescope rubric General marking principles (all questions) Code and discussions should be pitched at an appropriate level, based on concepts and principles as introduced in the course materials. Marks may be reduced for excessively verbose or overly technical implementations or discussions, or for excessively commented or overly documented code. Marks may be significantly reduced for discussions which do not align with associated code or output. Marks may be reduced for poor scholarship. 1.1 6: Comprehensive comments which clearly and correctly describe all elements of the code. 5: Intermediate mark 4: Comments which describe all or almost all elements of the code, with the majority of key elements described correctly. 3: Intermediate mark 2: Brief comments which describe at least a few elements of the code correctly. 1: At least one relevant comment. 0: Substantially incorrect or incomplete. 1.2 10: A comprehensive investigation of the problem. A clear and complete discussion which includes appropriate mathematical arguments where relevant, is fully supported with numerical evidence, and which draws appropriate conclusions from the numerical evidence. The discussion is supported with appropriate plots/output with appropriate formatting. Code is clear, concise, well formatted, and easy to understand. 9: Intermediate mark 8: Intermediate mark 7: A good investigation of the problem, but the investigation may be more limited in scope. The discussion is generally clear, draws appropriate conclusions, and includes some appropriate mathematical arguments where relevant, but may be lacking in a small number of aspects. A small number of conclusions may be overly strong, or may not be fully supported by numerical evidence. The discussion is supported with appropriate plots/output, which may be lacking in a small number of aspects. Code is generally clear, concise, well formatted, and easy to understand, but may be lacking in a small number of aspects. 6: Intermediate…

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Lab 3 Introduction library(tidyverse) ## -- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 -- ## v dplyr 1.1.4 v readr 2.1.6 ## v forcats 1.0.1 v stringr 1.6.0 ## v ggplot2 4.0.1 v tibble 3.3.1 ## v lubridate 1.9.4 v tidyr 1.3.2 ## v purrr 1.2.1 ## -- Conflicts ------------------------------------------ tidyverse_conflicts() -- ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag() ## i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors judges <-read.csv("judges.csv") Question 1 The dataset contains 224 observations and 12 variables. judges <- judges%>% mutate( gender_label =ifelse(woman==1, "Woman", "Man"), party_label =ifelse(republican==1, "Rep appointee", "Dem appointee") ) prop_table <- judges%>% group_by(gender_label, party_label)%>% summarise(count =n(), .groups = "drop")%>% group_by(gender_label)%>% mutate(proportion = count/ sum(count)) prop_table%>% select(gender_label, party_label, count, proportion) ## # A tibble: 4 x 4 ## # Groups: gender_label [2] ## gender_label party_label count proportion ## <chr> <chr> <int> <dbl> ## 1 Man Dem appointee 76 0.409 ## 2 Man Rep appointee 110 0.591 ## 3 Woman Dem appointee 27 0.711 ## 4 Woman Rep appointee 11 0.289 1 In terms of gender, males and females have different representation among Democratic and Republican appointees for judgeship (for instance, Republican presidents appoint more male judges). Women, however, tend to be more likely than men to be appointed as Democratic judges (i.e., Democrats appoint a higher proportion of females than Republicans do). Male judges tend to be appointed more often than female judges from Republican presidents; however, while females tend to have a higher proportion of being appointed Democrats, they also have the greatest adherence to a progressive voting record relative to male judges. Question 2 ggplot(judges,aes(x = progressive.vote))+ geom_histogram(bins = 20)+ labs(title = "Distribution of Progressive Vote Share Among Circuit Court Judges", x = "Progressive Vote Share", y = "Count") 0 10 20 0.00 0.25 0.50…