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
The Nernst equation represents the balance between the concentration gradient and the electrical gradient across a cell membrane.
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.
10
It's 2. your equation is y=mx+b, so the gradient, or slope, is the "m" in the equation.
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
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
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.