Practical Applications of Linear Models and Regression Analysis Cont.
After examining city-wide population trends, let’s turn our attention to the educational sector in Scenario 3. Here, we analyze high school graduation rates, applying similar regression techniques to understand trends in education. This scenario emphasizes the applicability of linear models in diverse fields, from urban planning to educational outcomes.
Scenario 3: High School Graduation Rates
The Department of Education is analyzing high school graduation rates across the state. You have access to the graduation rates and other relevant data for several high schools over the last five years.
| Year | Graduation Rate (%) |
|---|---|
| 2015 | [latex]77[/latex] |
| 2016 | [latex]80[/latex] |
| 2017 | [latex]85[/latex] |
| 2018 | [latex]88[/latex] |
| 2019 | [latex]91[/latex] |
Through these varied scenarios, you’ve gained hands-on experience in writing linear models, performing regression analysis, and understanding the importance of the coefficient of determination. Each scenario illustrated the practical applications of these mathematical concepts in different real-world contexts, from business and urban planning to education. Reflect on the power of data analysis and how it informs decision-making in various sectors, highlighting the crucial role of accurate and insightful statistical interpretation. Terrific work today!