1 Introduction

we used data from…. I propose following questions based on my understanding….. 1. What is the median debt of graduates? 2. What is the standard deviation of instructional spending per student? 3. what is the mean faculty salary? 4. what is Mean of in-state tuition? 5. Bar plot of Colleges by Region. 6. Histogram of Net Price? 7. How many colleges have over 60% female students? 8. What is the most common region colleges area located in? 9. What is the variance in average SAT scored? 10. What is the mean percentage of first-generation students?

Analysis

We will explore the questions in detail

college = read.csv("https://www.lock5stat.com/datasets3e/CollegeScores4yr.csv")
head(college)

Q1: What is the median debt of graduates?

median(college$Debt, na.rm = TRUE)
## [1] 713.5

The median of debt of graduates is $713.5. This tells us the typical student loan burden, the “middle” student. this is of course what they owe after completing a four-year degree.

###Q2: Standard Deviation of Instructional Spending

sd(college$InstructFTE, na.rm = TRUE)
## [1] 11095.06

The standard deviation of instructional spending is $11,905.06. This shows how spread out the colleges area in how much they spend on teaching per student. A large value would mean spending varies a lot across colleges.

###Q3: Mean Faculty Salary

mean(college$FacSalary, na.rm = TRUE)
## [1] 7465.778

The mean faculty salary is $7,465.78 which gives the idea of what professors make. This is at four year institutions and can reflect the financial investments of the schools in their teaching staff.

###Q4: Mean of In-State Tuition

mean(college$TuitionIn, na.rm = TRUE)
## [1] 21948.55

The mean in-state tuition was $21,948.55. This shows the cost of attending a college in the same state as a resident. This typically helps students with affordability.

###Q5: Bar plot of Colleges by Region

barplot(table(college$Region), main = "Colleges by Region", col = "orange", las = 2)

This bar plot visualizes how many colleges exist in each U.S. region, helping show the geographic distribution of higher education.And looking at it you can see the northeast is leading in attendance.

###Q6: Histogram of Net Price

hist(college$NetPrice, main = "Histogram of Net Price", xlab = "Net Price", col = "lightblue")

This histogram shows the spread of net prices students might expect. It helps highlight whether most colleges are affordable or expensive.

###Q7: How many colleges have over 60% female students?

mean(college$Female, na.rm = TRUE)
## [1] 59.29588

There are around 59 colleges with an over 60% female population. This result gives the average percentage of female students across all colleges. It’s a useful way to understand gender representation in higher education.

###Q8: What is the most common region colleges area located in?

names(sort(table(college$Region), decreasing = TRUE))
## [1] "Northeast" "Midwest"   "Southeast" "West"      "Territory"

This shows the most common geographic region among all colleges in the data set, helping us see where most schools are located. You can see the most common region is the northeast.

###Q9: What is the variance in average SAT scored?

var(college$AvgSAT, na.rm = TRUE)
## [1] 16617.2

The variance in average SAT scores is 16,617 which reflects how much SAT scores differ between colleges. A higher value means there’s more variation in how strong the typical student body is academically.

###Q10: What is the mean percentage of first-generation students?

mean(college$FirstGen, na.rm = TRUE)
## [1] 33.55713

The mean percentage of first-generation students is 33.56%.This gives the average percentage of students who are the first in their family to attend college. It gives insight into how inclusive and accessible colleges are for new generations.

Summary

In this project, I used basic statistics to explore different parts of the U.S. four-year Colleges. I looked at costs, salaries, admission rates, and student demographics. using numbers like the mean, median, and variance helped me understand the data better. I also used a few visuals to see some patterns of the data. Overall, this helped me learn how colleges can be different from region to region. Looking into colleges is very smart for upcoming students.