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日期：2024-03-07 09:48

Project Proposal (Video Presentation)

Due: March 15, 2024 by 11:59PM ET

Goal of the Assessment:

This assignment consists of two parts. The goal of the first part is to give you a head start with

your final project. This will be accomplished by finding an area of interest to study and realworld data to work with. The second part of this assignment will provide you with an

opportunity to conduct research in an area you’re interested in. Conducting research will help

you determine what has been accomplished regarding your question and to highlight the

importance of your proposal.

The stepsinvolved in completing this assignment encompassthe general process of proposing

a research question and will help to form the basis for a strong introduction section in your

final project report. Your task for this assignment is to prepare a video presentation that describes your

data and research topic of interest. Completing this assignment will also give youthe chance to think

about the appropriateness of generalized linear models (GLM) as a tool for answering your

proposed research question using your chosen data. Lastly, this assignment provides an

opportunity to getsome feedback on your research question that can be used to improve your

final report using peer reviews.

Assignment Instructions:

1. Decide on one (or a few possible) areas of interest that you may want to explore.

These areas of interest can be anything that matters or is of interest to you. Some

examples could be (but are not limited to) sports, medicine, public health, economics,

video games, literature, etc. Pick something that you truly care about.

2. Next, think about possible research questions you may want to study regarding these

areas of interest. What do you want to know about these areas of interest? For

example, you want to make sure that your question can be answered/studied using

generalized linear models. GLM is not applicable for all datasets. So, you’ll want to

frame your question to be something related to modelling a relationship or classifying

a categorical outcome based on thisrelationship. You’ll also want to consider whether

the variable of interest would allow the assumptions of GLM to hold.

3. After producing a research question, you will need to find some open-source data that

you may use in your data analysis. You want to make sure that the data you find has

both: 1) your response variable of interest (or has variables that could be used to

create that variable), and 2) any other variables you may want to use as predictors. By

looking for data online, you may realize you need to modify your research question

slightly or pick another one if you can’t quite find the data you’re looking for.

Alternatively, if you are having trouble finding data online but want to stick with this

research question, be sure to mention that you expect there to be many limitations to

the dataset because it doesn’t quite meet your needs. Step 4 can also help you decide

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what predictors might be needed for you to answer your question.

To help you identify data for your research question, some examples of open data

sources are listed below:

o https://open.toronto.ca for freely accessible data from Toronto

o https://data.ontario.ca for freely accessible data from Ontario

o https://www150.statcan.gc.ca/n1/en/type/data?MM=1 for data collected by

Statistics Canada

o https://sports-statistics.com/sports-data/ for various sports-related datasets

o https://data.oecd.org for data on various country-level variables

o https://mdl.library.utoronto.ca for links to many other data portals through

the University of Toronto library

4. Once you’ve found your dataset and have decided on your research question, perform

a literature search about to learn more about your research question. A literature

search can be done by performing a search on the University of Toronto library website

(https://onesearch.library.utoronto.ca/) or other databases that feature scholarly

articlesto learn about anything related to your area of interest and research question.

Look for academic papers or published reports (i.e., preferably peer-reviewed work

that has been published in reputable scholarly journals, not websites, blogs, or news

articles, etc.) that studied the same research question or something related, that

describes you more about what you may need to consider in your analysis. In your

literature review, include academic papers or reports to justify why your research

question is important. Some other suggestions on performing a literature review

include:

o Focus on giving your reader a rough idea of how many academic papers have

studied your research topic (or closely related concepts to your topic). This

process of looking at the number of academic papers which describe a specific

topic tells your audience how popular the area of research is and how much

research has been done.

o Give examples from a few important papers about what was found or

discovered to be important in relation to your question. This can be important

variables, important results, surprising results, etc. The process of identifying

and describing important paperstells your audience that you are aware of prior

results and that you will be using these to plan your analysis.

o Think about how your research question fits into the general area of research

about your topic. For example, is your research question different from

research questions in other studies? If so, how? A novel research question

consists of either something that: 1) nobody has studied before, 2) studied

using a different methodology/study design, or 3) studied in a different population.

The process of examining if your research question is novel tells your audience

that you see the importance of what you are researching and can frame it

against what has already been done.

Attached here are some additional library resources which may be helpful for

performing a literature review::

o https://guides.library.utoronto.ca/librarysearchtips/gettingstarted for more

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details about searching for articles related to your question

o https://guides.library.utoronto.ca/citing for details about why and how to

cite your references

o https://guides.library.utoronto.ca/c.php?g=251103&p=1673071 for help

getting the correct citation format

5. After completing a literature review, perform a short exploratory data analysis of your

chosen dataset. You will want to focus on identifying anything that you may need to

consider moving forward. This includes identifying in your dataset:

a. potential confounders,

b. statistical outliers of the exposure and the confounders of interest (if

continuous),

c. variables with high spread or observations that don’t make sense, and

d. missing data

For section 5, you want to make sure you specifically mention the presence of any of

the characteristics in 5a-d (or lack thereof) and what this means for the analysis you

will eventually perform. For example, this may include describing how any of the

characteristics in 5a-d might cause problems (or not) with the results of GLM or

generalizability. You will need to present univariate or bivariate numerical and/or

graphical summaries describing the variables. Choose the options that highlight the

features of the data that you want to point out but will also let your reader clearly

understand the data that you will be working with.

Guidelines for Picking a Dataset

o Government data portals often contain many datasets about diverse topics – if one

dataset doesn’t have all the variables you might want to consider, feel free to

combine different datasets together

o When combining datasets, make sure that each unit being measured isthe same

in both datasets (i.e., it’s reasonable that both measurements are on the same unit)

o There are many data repositories online – if you find a dataset there that is of interest

to you, you MUST ensure that your question is different than what the dataset was

originally used for.

o YOU MAY NOT use any dataset that is part of any R package or library, or that is

contained in a textbook. If you’re not sure, please ask the instructor.

o You will need to make sure you have enough variables to be able to showcase the

statistical methods that you will learn later in the course. Some topics the teaching

team will require include model validation and model refinement so please ensure

your dataset has at least 5 predictor variables.

o You will also need to make sure you have enough observations to be able to validate

your model, which will involve splitting your dataset into two roughly equal parts.

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Presentation Content Requirements:

Your presentation should satisfy the following requirements:

o The presentation should be organized clearly (consider using headings or sections)

and include the following information:

a. Your research question, why you chose it (i.e., why it’s of interest to you), and

why it may be of interest to others.

b. Summaries of academic papers related to your question or topic, highlighting

similarities/differences to what you propose, and how you will incorporate this

knowledge into your model/project.

c. Details and summaries on your chosen dataset including the variables

collected, the number of observations and anything that stands out in the data

that would need to be addressed/investigated further in your analysis.

d. A discussion about how and why a GLM fits your chosen data. This will allow

you to answer your proposed research question, as well as whether you

anticipate any problems that may arise in your analysis from EDA.

e. References for where you located the data, and your background research on

your topic

o The presentation should be presented for an audience that has some statistics

background but is not necessarily familiar with the area of your research question or

GLMs.

o The presentation should contain figures and/or tables with proper labels/titles as

appropriate in your Data Description - Exploratory Data Analysis section

o The presentation should have references listed in proper APA format, and

o The presentation itself should not contain R codes

Technical Requirements:

Your submission to Quercus should include the following:

1. A video that presents your proposed research area and question, the dataset you have

chosen, and the exploration of your dataset.

o The video should be no more than 5 minutes in length

o Youmust display yourUof T StudentID card(or other valid government-issued

photo ID) at the beginning of your video The presenter’s face must be visible

throughout the video

o The presentation should include an appropriate visual medium (e.g., slides) to

display important information in an easily readable way.

o The video should be hosted on a video-sharing service (e.g., MS Streams,

MyMedia are supported by the university)

2. The proposed dataset you will use in your Final Project, as a csv or xlsx file, or if too

large, as a link to cloud storage where the dataset is saved in csv or xlsx.

3. A copy of the slides/visual aids used in your presentation saved as a PDF document.

4. The R Markdown file containing the code used to produce your exploratory data

analysis and tables/figures.

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How to upload different components of this assignment:

o A link to your video should be added as a comment to your submission. This can be

done via MS Stream or MyMedia.

o Instructions for uploading to MS Stream: https://learn.microsoft.com/enus/stream/portal-upload-video

o Instructions for uploading to MyMedia: https://itoengineering.screenstepslive.com/s/ito_fase/a/1291600-how-do-i-upload-avideo-or-audio-file-to-mymedia

o Both require you to log in with your UofT credentials.

o The R Markdown File should be added as a file upload on the assignment page on

Quercus

o The slides used in presentation should be added as a file upload on the assignment

page on Quercus

o The dataset you chose to work with should be uploaded either as a file upload to

the assignment page on Quercus OR as an attachment to a comment on your

assignment submission. Attaching the file as a comment is best if the dataset is

large (>3Mb in size)