Differential Equations
What is the primary use of differential equations in mathematical modeling, as described in the provided text?
To describe physical situations using traditional language.
To relate a quantity's rate of change to itself.
To confirm only predictable actions.
To avoid logical derivations.
Which of the following scenarios is best modeled using a differential equation?
The constant speed of a car on a highway.
The population growth of a bacteria colony where the growth rate is proportional to the current population.
The fixed interest rate on a savings account.
The trajectory of a ball thrown in a vacuum.
A scientist observes that the rate of decay of a radioactive substance is proportional to the amount of the substance remaining. Which type of equation would be most suitable to model this phenomenon?
A linear equation.
A quadratic equation.
A differential equation.
An integral equation.
Which of the following differential equations corresponds to the slope field where the slopes are always equal to the x-coordinate at any given point?
For the differential equation , in which region of the xy-plane are the slopes in the slope field positive?
Only in the first quadrant.
Where .
Where .
Only when and
A slope field is shown with horizontal line segments along the line y = x. Which of the following differential equations could the slope field represent?
Using Euler's method with a step size of 0.1, approximate y(1.1) for the differential equation with the initial condition y(1) = 2.
2.0
2.1
2.105
2.2

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Consider the differential equation with initial condition y(0) = 1. Using Euler's method with a step size of h = 0.2, approximate y(0.4).
1.00
1.20
1.44
1.96
Given the differential equation with initial condition y(0) = 1, you want to approximate y(1) using Euler's method. To ensure your approximation is within 0.1 of the actual value, which strategy is most appropriate?
Use a fixed step size of h = 0.5.
Use a step size of h = 1.0.
Start with a step size of h = 0.2 and halve the step size iteratively, comparing successive approximations until they are within a small tolerance (e.g., 0.01) of each other.
There is no way to ensure the approximation is within 0.1 of the actual value.