The image you uploaded shows a problem that involves modeling data on the number of Internet of Things (IoT) devices from 2015 to 2025. The table provides years and the corresponding number of devices in billions, and you're asked to find the best-fitting cubic model for the data, using $x$ as the number of years after 2015, and $y$ as the billions of devices connected.

The data table provided:

• Year (2015-2025) and Devices (Billions)
  • 2015: 15.42
  • 2016: 17.67
  • 2017: 20.36
  • 2018: 23.04
  • 2019: 26.65
  • 2020: 30.71
  • 2021: 35.83
  • 2022: 42.63
  • 2023: 51.12
  • 2024: 62.11
  • 2025: 75.42

You are asked to:

• a. Find the best-fitting cubic model for the data, with $x$ equal to the number of years after 2015 and $y$ equal to the billions of devices.

To approach this, we'll need to:

1. Set $x = 0$ for the year 2015, $x = 1$ for 2016, and so on until $x = 10$ for 2025.
2. Fit a cubic regression model of the form:
   $y = ax^3 + bx^2 + cx + d$ where $y$ represents the billions of devices, and $x$ is the number of years after 2015.

I can set up and calculate this cubic regression model for you. Would you like to proceed with that approach, or do you need any clarifications?