# Data Management University Project

## Introduction

The purpose of this project, is to collect raw data about five universities and use matrixes to rank them accordingly. The five universities I have researched are...

## Criteria: Entrance Average

Weighting: 30%

Formula: s=10-|(80-x)|

Let (s) represent the score.

Let (x) represent the university data.

Why Entrance Average:

The entrance average is an important piece of criteria for my university selection therefore it has the highest weighting, 30%. This piece of information will allow me to know what marks will be required to gain acceptance to my Universities of choice and will me a standard to strive for in my final year of school. Another benefit to knowing the entrance average is that it will help me judge the type of people who I will be attending my classes with and the competitions I will face.

The Formula:

The (10 -) gives us the highest possible score to be achieved that being 10. The (80) is my constant which represents an 80% final grade. The (80-x) ends up subtracting the constant from the university data input. Finally to ensure that 80-x measures a distance, I take the absolute value of the (80-x) to ensure the result is positive.

Example: University of Toronto

s=10-|(80-x)|

s=10-|(80-81)|

s=10-|-1|

s=10-1

s=9

## Criteria: Tuition Fees

Weighting: 20%

Formula: s=10-|((x-4800)/1000)|

Let (s) represent the score.

Let (x) represent the university data.

Why Tuition Fees:

The cost of tuition fees are weighted at only 10% because my parents have been adamant about how they will pay for my university no matter what. However I want to make sure the University isn't outrageously priced so that I am not putting too much strain on my parents.

The Formula:

Just like in every other formula, the (10 -) will give me the final score out of 10 and I also take the absolute value to ensure the end result is positive so that my results don't get skewed. The x again represents the university data subtracted by my constant which is \$4800. I choose to make my constant \$4800 as after collecting the raw data I created a value roughly in the middle to ensure that my differences would all be in the range of 0-10. Finally to make the numbers smaller, I divided by 1000 bringing the end result closer to my wanted range.

Example: Queens University

s=10-|((x-4800)/1000)|

s=10-|((5675-4800)/1000)|

s=10-|((875)/1000)|

s=10-|0.875|

s=9.13

## Criteria: Total Amount of Full Time Students

Weighting: 10%

Formula: s=10-|((x-32500)/10000)|

Let (s) represent the score.

Let (x) represent the university data.

Why Amount of Students:

As I have spent most of my school career at HTS, I have become used to small tight-knit communities which is why this criteria is weighted at 20%. By knowing the total amount of full time students, I will be able to see how close to HTS the university will be. Also the smaller the amount of students, the more I will be able to interact with my professors to create ties to use later on in life.

The Formula:

For this formula I again take the absolute value of the end result to keep my results positive. I made my constant 32500 as I know that Universities have high populations and making my constant high would be taking that into account. Just like my other formula's, I subtract my raw data from my constant to gain my difference and follow with creating a number between 0-10 by dividing by 10000.

Example: Western University

s=10-|((x-32500)/10000)|

s=10-|((23000-32500)/10000)|

s=10-|((-9500)/10000)|

s=10-|-0.95|

s=10-0.95

s=9.05

Weighting: 20%

Formula: s=10-((x-60)/10

Let (s) represent the score.

Let (x) represent the university data.

The graduation ratio of universities is important for me as I want to ensure that where I go is good at keeping students and also has a good record of people who complete the whole university process.

The Formula:

First off, unlike the other four formulas this one did not need to take the absolute value as my constant of 60% was lower than all the universities meaning all the numbers would already be positive. The rest of the formula is similar to the others in which I subtract my raw data from the constant and divide by a certain number, in this case 10, to create a number between 0 and 10.

Example: McGill University

s=10-((x-60)/10)

s=10-((71-60)/10)

s=10-(11/10)

s=10-1

s=9

## Criteria: Distance from Home

Weighting: 20%

Formula: s=10-|(75-x)/100|

Let (s) represent the score.

Let (x) represent the university data.

Why Distance from Home:

Over my whole time at school, I have been very close to home so naturally I want to keep that trend going through university. To me staying close to home will be able to keep a reality check going as I will be close to my parent who would support me through any issues I have in university. However, I only weighted this criteria at 20% since I do know that I need to become more independent by being far away from home and thus am willing to move away if need be.

The Formula:

First off, I made the constant 75km away to stay in between being far away and close to home. By taking the absolute value of 75 - x, I keep the answer positive allowing my results out of 10 to make logical sense. Finally like the other equations I divide the result by 100 to create a number that can fit the range of 0-10.

Example: York University

s=10-|(75-x)/100|

s=10-|(75-49.2)/100|

s=10-|(25.8)/100|

s=10-|0.258|

s=9.75

## Standings:

1st: University of Toronto

2nd: Western University

3rd: York University

4th: Queens University

5th: McGill University

## Conclusion:

After compiling all my raw data and multiplying the matrices, I was able to conclude that the best university for me is the University of Toronto. This result coincides with my personal belief as U of T is my number one Canadian university to attend and the results only further my desire to go there. Another University in my top 3 is Western coming in at number two. Looking at the results I agree that Western should be placed this high as it has the closest tuition to my preferred price and also the second closest amount of full-time students. In third was surprisingly the bottom of my personal top five list, York. I was surprised to see it claiming the third spot but realized that these are only five out of many different criteria. The most shocking placement, was Queens at fourth place. This was so surprising since to me both Queens and U of T are the two Canadian universities that I want to go to the most and seeing Queens being statistically so low was shocking. Finally at fifth place was the university I expected to see there, McGill. Based on my personal preferences, I knew that McGill wasn't really a contender yet I still wanted to see how it added up to my preferred universities. However the distance from home and entrance average put it out of the running completely.

Finally I must say that this project while not necessarily changing my university choices, has provided me with much more information about the ones I want to attend along with putting them all into perspective of each other.