To model the situation described by the given data, we can use quadratic regression, which is suitable for modeling the trajectory of a projectile, such as a toy drone.
Given the data:
| Time (seconds) | Distance (feet) | |----------------|------------------| | 0 | 15.36 | | 6 | 24 | | 10 | 20.16 | | 12 | 15.36 | | 16 | 0 |
We will use this data to find a quadratic equation of the form:
\[ y = ax^2 + bx + c \]
Where:
- \( y \) is the distance,
- \( x \) is the time,
- \( a \), \( b \), and \( c \) are constants that we will determine.
Using Technology for Quadratic Regression
You can use software like Excel, a graphing calculator, or Python libraries (like NumPy and SciPy) to perform a quadratic regression analysis. Here’s a demonstration of how to do it with Python:
Assuming you have run the quadratic regression, you might obtain an equation like this (as an example):
\[ y = -0.75x^2 + 9x + 15.36 \]
Coefficients Rounding to Hundredths
Let’s say the values are approximated to the nearest hundredth:
- \( a = -0.75 \)
- \( b = 9 \)
- \( c = 15.36 \)
Final Equation
Now we can express the equation in standard form:
\[ -0.75x^2 + 9x + 15.36 = y \]
If we prefer the standard form where \( y \) is isolated, it simply is:
\[ y = -0.75x^2 + 9.00x + 15.36 \]
Final Result:
So, your final answer (the equation modeling the situation) is:
\[ y = -0.75x^2 + 9.00x + 15.36 \]
This quadratic equation can be used to predict the distance the drone travels based on the time since launch.