The document analyzes factors that affect university enrollment at the University of New Mexico from 1981 to 2009. It ranks high school graduates, per capita income, and unemployment rate as top predictors of enrollment. Models showing relationships between enrollment and each of these factors are presented. High school graduates and per capita income have strong positive correlations with enrollment, while unemployment has a weak negative correlation. Per capita income and high school graduates are identified as the most effective predictors of enrollment at 94% and 91% accuracy respectively. The document concludes by recommending continued focus on these top predictors for planning future enrollment.

1. University Enrollment Kristin Summa and Cassandra Scarantino

2. Background Information University of New Mexico Undergrad enrollment Data from years 1981 to the current 2009 Known variables: January unemployment rates June High School graduates monthly per capita income in Albuquerque

3. Predictions Ranking of predictions: High school graduates Per capita income Unemployment rate Predictions were made before calculations and are subject to change upon data analysis

4. Enrollment vs. Unemployment

5. Step 1: Describe x & y variables Y: enrollment X: unemployment rate Step 2: Describe the relationship there is a weak negative correlation Step 3: Calculate Enrollment vs. Unemployment

6. Enrollment vs. Unemployment Step 4: How effective is this model? Standard deviation of error is high meaning that it might not be our best way to predict enrollment Step 5: What is the significance? The model is significant because the p-value is lower that alpha which is .05 Step 6: What is the significance of the x variable? The highest enrollment rate would be 28,336 For every 1 unemployed person the enrollment rate goes down 1,889 enrollment = 28,336-1,889 (unemployment rate)

7. Enrollment vs. High School Graduates

8. Step 1: Describe x & y variables Y: Enrollment X: High School Graduates Step 2: Describe the relationship There is a strong positive correlation Step 3: Calculate Enrollment vs. High School Graduates

9. Step 4: How effective is this model? Standard deviation of error is high but compared to other models it be our best option Step 5: What is the significance? It is very significant because the p-value is extremely low and definitely smaller than .05Step 6: What is the significance of the x variable? Step 6: What is the significance of the x variable? Supports the idea that when there are fewer high school graduates college enrollment will be down enrollment = -4,481.60+1.22 (High School Graduates) Enrollment vs. High School Graduates

10. Enrollment vs. Per Capita Income

11. Step 1: Describe x & y variables Y: Enrollment X: Per Capita Income Step 2: Describe the relationship There is a strong positive correlation Step 3: Calculate Enrollment vs. Per Capita Income

12. Step 4: How effective is this model? Standard deviation of error is high but compared to other models it be our best option Step 5: What is the significance? It is very significant because the p-value is extremely low and definitely smaller than .05 Step 6: What is the significance of the x variable? Supports the idea that when a households cannot afford college they will not be able to send their children to college enrollment = -4,481.60+1.22 (High School Graduates) Enrollment vs. Per Capita Income

13. Conclusion Prediction of enrollment Per capita income – 94% Low income impacts enrollment High school enrollment – 91% Graduating class size Unemployment – 77% Correlation Coefficient

14. Recommendations Per capita income Family income High school enrollment Population size Recruitment Unemployment United States unemployment rate