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