Mastering Exponential Growth and Regression Analysis Cont.
Having explored the financial growth of a tech startup through regression models, let’s shift our focus to a critical environmental issue. In Scenario 2, we analyze CO2 emission data, a different type of dataset that presents its own unique challenges.
Scenario 2: Environmental Data Analysis
An environmental agency has been monitoring the CO2 levels in a city. The data collected over several years seems to follow a continuous growth pattern.
| Year | CO2 Level (ppm) |
|---|---|
| 2013 | [latex]400[/latex] |
| 2014 | [latex]405[/latex] |
| 2015 | [latex]410[/latex] |
| 2016 | [latex]416[/latex] |
| 2017 | [latex]423[/latex] |
| 2018 | [latex]431[/latex] |
| 2019 | [latex]440[/latex] |
| 2020 | [latex]450[/latex] |
| 2021 | [latex]461[/latex] |
As we conclude this activity, reflect on the insights gained from applying exponential and linear regression models to different types of data. Consider the importance of choosing the right model to accurately interpret and forecast trends, and the implications these models have in real-world decision-making. Through these exercises, you’ve not only honed your skills in regression analysis but also gained a greater appreciation for its significance in making informed predictions and strategies in business, environmental science, and beyond. Terrific work today!