The equation for the gradient of a linear function mapped in a two dimensional, Cartesian coordinate space is as follows.
The easiest way is to either derive the function you use the gradient formula
(y2 - y1) / (x2 - x1)
were one co-ordinate is (x1, y1) and a second co-ordinate is (x2, y2)
This, however, is almost always referred to as the slope of the function and is a very specific example of a gradient. When one talks about the gradient of a scalar function, they are almost always referring to the vector field that results from taking the spacial partial derivatives of a scalar function, as shown below.
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The equation for the gradient of a function, symbolized ∇f, depends on the coordinate system being used.
For the Cartesian coordinate system:
∇f(x,y,z) = ∂f/∂x i + ∂f/∂y j + ∂f/∂z k where ∂f/(∂x, ∂y, ∂z) is the partial derivative of f with respect to (x, y, z) and i, j, and k are the unit vectors in the x, y, and z directions, respectively.
For the cylindrical coordinate system:
∇f(ρ,θ,z) = ∂f/∂ρ iρ + (1/ρ)∂f/∂θ jθ + ∂f/∂z kz where ∂f/(∂ρ, ∂θ, ∂z) is the partial derivative of f with respect to (ρ, θ, z) and iρ, jθ, and kz are the unit vectors in the ρ, θ, and z directions, respectively.
For the spherical coordinate system:
∇f(r,θ,φ) = ∂f/∂r ir + (1/r)∂f/∂θ jθ + [1/(r sin(θ))]∂f/∂φ kφ where ∂f/(∂r, ∂θ, ∂φ) is the partial derivative of f with respect to (r, θ, φ) and ir, jθ, and kφare the unit vectors in the r, θ, and φ directions, respectively.
Of course, the equation for ∇f can be generalized to any coordinate system in any n-dimensional space, but that is beyond the scope of this answer.
It's 2. your equation is y=mx+b, so the gradient, or slope, is the "m" in the equation.
Assume you want to know what is the formula of the gradient of the function in multivariable calculus. Let F be a scalar field function in n-dimension. Then, the gradient of a function is: ∇F = <fx1 , fx2, ... , fxn> In the 3-dimensional Cartesian space: ∇F = <fx, fy, fz>
No, this is not a function. The graph would have a vertical line at x=-14. Since there are more than one y value for every given x value, the equation does not represent a function. The slope of the equation also does not exist.
The [ 2x + 1 ] represents a function of 'y' .
When the equation is in the form "y = mx + c" the intercept is given by 'c' (and the gradient by 'm'): 3x + 3y = 9 ⇒ x + y = 3 ⇒ y = -x + 3 ⇒ Intercept is 3 (And the gradient is -1)
The answer will depend on the context. If the curve in question is a differentiable function then the gradient of the tangent is given by the derivative of the function. The gradient of the tangent at a given point can be evaluated by substituting the coordinate of the point and the equation of the tangent, though that point, is then given by the point-slope equation.
y = 4x + 2 It has a slope (gradient) or 4. The slope/gradient of a linear function is simply the number in front of the x when the equation is in the form y=mx+b. (the coefficient of x).
An equation such as y = mx + c is said to be in standard form. From such an equation, Gradient = coefficient of x = 3
Gradient= change in field value divided by the distance
If necessary, rearrange the linear equation so that it is in the slope-intercept form: y = mx + c Then the gradient of the line is m.
It's 2. your equation is y=mx+b, so the gradient, or slope, is the "m" in the equation.
10
If you have the equation, yes. If the equation is given in terms of x and y, make y the subject of the equation. That is, expres the equation in the form y = mx + c where m and c are constants. Then the gradient is m.
15
When the gradient is big, it means that there is a steep change in the value of a function with respect to its variables. This indicates that the function is changing rapidly over a small distance. A big gradient suggests that the function is highly sensitive to changes in its inputs.
Assume you want to know what is the formula of the gradient of the function in multivariable calculus. Let F be a scalar field function in n-dimension. Then, the gradient of a function is: ∇F = <fx1 , fx2, ... , fxn> In the 3-dimensional Cartesian space: ∇F = <fx, fy, fz>
y = 11x + 5 The slope/gradient of this equation is 11. The slope/gradient can easily been seen in a linear equation: it is simply the co-efficient of x