Project_2: Statistical Analysis of Male and Female Students Enrolled at Montgomery College (Year 2015)
Marvellous Onajobi
2025-11-15
Description
- This project performs a Chi-Square statistical analysis on the counts of male and female students between the ages of 21–24 enrolled at Montgomery College in the year 2015, specifically at the Rockville Campus. The analysis uses the “Montgomery_College_Enrollment_Data” dataset from data.montgomerycountymd.gov.
Objective
- The goal is to determine whether the proportion of students within the 21–24 age group is grater for males than it is for females.
Introduction
Montgomery College is one of the most diverse community colleges in the state of Maryland, attracting students from a wide range of backgrounds, cultures, and age groups. As the Rockville Campus continues to grow, understanding the demographic breakdown of its student population becomes increasingly important, especially when looking at how different age groups and genders contribute to overall enrollment.
In this project, I focus specifically on students between the ages of 21 and 24 in the year 2015, a group that often represents individuals transitioning from early adulthood into the workforce or continuing their academic journey.
By applying a Chi-Square test, the aim is to determine whether the distribution of male and female students in this age range differs significantly, and whether one group is more represented than the other. This analysis highlights the enrollment pattern at the Rockville Campus and provides insight into how gender representation varies within this segment of the student population.
Fields used:
Gender— CategoricalAttending Rockvile— CategoricalAge Group— Categorical
Hypotheses (ANOVA style):
\(H_0\): \(p_1\) \(=\) \(p_2\)
\(H_a\): \(p_1\) \(>\) \(p_2\)
Level of Significance \(\alpha\): \(=\) \(0.05\)
Packages and data load
# Load required libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.1
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(ggplot2) # Set working directory
setwd("~/Downloads/25_Semesters/Fall/DATA101")
# Load the dataset
MC_Data <- read_csv("Montgomery_College_Enrollment_Data.csv")## Rows: 25320 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): Student Type, Student Status, Gender, Ethnicity, Race, Attending G...
## dbl (2): Fall Term, ZIP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Create a working copy
df <- MC_Data# Examine the structure of the dataset
str(df)## spc_tbl_ [25,320 × 18] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Fall Term : num [1:25320] 2015 2015 2015 2015 2015 ...
## $ Student Type : chr [1:25320] "Continuing" "Continuing" "Continuing" "New" ...
## $ Student Status : chr [1:25320] "Full-Time" "Part-Time" "Part-Time" "Full-Time" ...
## $ Gender : chr [1:25320] "Female" "Male" "Male" "Male" ...
## $ Ethnicity : chr [1:25320] "Not Hispanic" "Not Hispanic" "Not Hispanic" "Not Hispanic" ...
## $ Race : chr [1:25320] "White" "White" "Black" "Asian" ...
## $ Attending Germantown : chr [1:25320] "Yes" "No" "No" "No" ...
## $ Attending Rockville : chr [1:25320] "Yes" "Yes" "Yes" "Yes" ...
## $ Attending Takoma Park/SS: chr [1:25320] "No" "No" "No" "No" ...
## $ Attend Day or Evening : chr [1:25320] "Day Only" "Evening Only" "Day & Evening" "Day Only" ...
## $ MC Program Description : chr [1:25320] "Health Sciences (Pre-Clinical Studies)" "Building Trades Technology (AA & AAS)" "Computer Gaming & Simulation (AA - All Tracks)" "Graphic Design (AA, AAS, & AFA - All Tracks)" ...
## $ Age Group : chr [1:25320] "25 - 29" "21 - 24" "20 or Younger" "20 or Younger" ...
## $ HS Category : chr [1:25320] "Foreign Country" "MCPS" "MCPS" "MCPS" ...
## $ MCPS High School : chr [1:25320] NA "Sherwood High School" "Quince Orchard Sr High School" "Thomas Sprigg Wootton High Sch" ...
## $ City in MD : chr [1:25320] "Bethesda" "Olney" "Gaithersburg" "North Potomac" ...
## $ State : chr [1:25320] "MD" "MD" "MD" "MD" ...
## $ ZIP : num [1:25320] 20816 20832 20877 20878 20906 ...
## $ County in MD : chr [1:25320] "Montgomery" "Montgomery" "Montgomery" "Montgomery" ...
## - attr(*, "spec")=
## .. cols(
## .. `Fall Term` = col_double(),
## .. `Student Type` = col_character(),
## .. `Student Status` = col_character(),
## .. Gender = col_character(),
## .. Ethnicity = col_character(),
## .. Race = col_character(),
## .. `Attending Germantown` = col_character(),
## .. `Attending Rockville` = col_character(),
## .. `Attending Takoma Park/SS` = col_character(),
## .. `Attend Day or Evening` = col_character(),
## .. `MC Program Description` = col_character(),
## .. `Age Group` = col_character(),
## .. `HS Category` = col_character(),
## .. `MCPS High School` = col_character(),
## .. `City in MD` = col_character(),
## .. State = col_character(),
## .. ZIP = col_double(),
## .. `County in MD` = col_character()
## .. )
## - attr(*, "problems")=<externalptr># Display column names
names(df)## [1] "Fall Term" "Student Type"
## [3] "Student Status" "Gender"
## [5] "Ethnicity" "Race"
## [7] "Attending Germantown" "Attending Rockville"
## [9] "Attending Takoma Park/SS" "Attend Day or Evening"
## [11] "MC Program Description" "Age Group"
## [13] "HS Category" "MCPS High School"
## [15] "City in MD" "State"
## [17] "ZIP" "County in MD"# Display the first and last few rows
head(df, 5)## # A tibble: 5 × 18
## `Fall Term` `Student Type` `Student Status` Gender Ethnicity Race
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 2015 Continuing Full-Time Female Not Hispanic White
## 2 2015 Continuing Part-Time Male Not Hispanic White
## 3 2015 Continuing Part-Time Male Not Hispanic Black
## 4 2015 New Full-Time Male Not Hispanic Asian
## 5 2015 New Full-Time Female Hispanic White
## # ℹ 12 more variables: `Attending Germantown` <chr>,
## # `Attending Rockville` <chr>, `Attending Takoma Park/SS` <chr>,
## # `Attend Day or Evening` <chr>, `MC Program Description` <chr>,
## # `Age Group` <chr>, `HS Category` <chr>, `MCPS High School` <chr>,
## # `City in MD` <chr>, State <chr>, ZIP <dbl>, `County in MD` <chr>tail(df, 5)## # A tibble: 5 × 18
## `Fall Term` `Student Type` `Student Status` Gender Ethnicity Race
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 2015 HS Student Part-Time Female Not Hispanic Black
## 2 2015 Continuing Full-Time Male Not Hispanic Asian
## 3 2015 New Full-Time Male Not Hispanic White
## 4 2015 Continuing Full-Time Male Hispanic Black
## 5 2015 HS Student Part-Time Male Not Hispanic White
## # ℹ 12 more variables: `Attending Germantown` <chr>,
## # `Attending Rockville` <chr>, `Attending Takoma Park/SS` <chr>,
## # `Attend Day or Evening` <chr>, `MC Program Description` <chr>,
## # `Age Group` <chr>, `HS Category` <chr>, `MCPS High School` <chr>,
## # `City in MD` <chr>, State <chr>, ZIP <dbl>, `County in MD` <chr>Data Cleaning
- Filtering the dataset to include only students who are attending the Rockville Campus and who fall within the 21–24 age group. Then, selecting the relevant fields such as: Gender, Age Group, and Campus Location.
# Keep only relevant columns and remove missing values
df2 <-df |>
filter(!is.na(Gender),
!is.na(`Age Group`),
!is.na(`Attending Rockville`),
`Attending Rockville` == "Yes",
`Age Group` == "21 - 24",
`Gender` %in% c("Male", "Female") )|>
select(Gender, `Age Group`, `Attending Rockville`) |>
group_by(Gender) |>
summarise(
count_of_students = n()
) |>
arrange(desc(count_of_students))
df2## # A tibble: 2 × 2
## Gender count_of_students
## <chr> <int>
## 1 Male 2295
## 2 Female 1988# Observed counts
observed <- df2$count_of_students
# Null hypothesis: equal proportion of males and females
theoretical_prop <- c(0.5, 0.5)
# Expected values
expected_values <- round(theoretical_prop * sum(observed),0)
expected_values## [1] 2142 2142Results
# Chi-Square Test
chi_result <- chisq.test(observed)
chi_result##
## Chi-squared test for given probabilities
##
## data: observed
## X-squared = 22.005, df = 1, p-value = 2.719e-06Visualization
This depicts students attending the Rockville Campus who are in the 21–24 age group. It keeps the key columns:
Gender,Age Group, andAttending Rockvilleand then groups the data byGenderin order to analyze the distribution of male and female students.As shown by the ledgend:
Males - represented by Blue
Females - represented by Purple
ggplot(df2, aes(x = Gender, y = count_of_students, fill = Gender)) +
geom_col(color = "black", width = 0.6) +
scale_fill_manual(values = c("Male" = "#1f77b4", "Female" = "#EF94FB")) +
labs(title = "Students (Age 21-24) at Rockville Campus in 2015",
x = "Gender",
y = "Number of Students") +
theme_minimal()
Conclusion
The Chi-Square test shows a significant difference in gender representation among students aged 21–24 at the Rockville Campus. With X-squared = 22.005, df = 1, p-value = 2.719e-06, which is less than the typical significance level of 0.05, we reject the null hypothesis that the proportions of students enrolled in the year 2015 were equal. This indicates that male students were significantly more represented than female students in this age group, highlighting a slight gender imbalance in enrollment.
Males - 2295
Females - 1988