Slope

There seems to be no clear answer as to why the letter m is used for slope, but it first appears in English in O'Brien (1844) who introduced the equation of a line as , and it can also be found in Todhunter (1888) who wrote "y = mx + c". == Definition ==

The slope of a line in the plane containing the x and y axes is generally represented by the letter m, and is defined as the change in the y coordinate divided by the corresponding change in the x coordinate, between two distinct points on the line. This is described by the following equation: :m = \frac{\Delta y}{\Delta x} = \frac{\text{vertical} \, \text{change} }{\text{horizontal} \, \text{change} }= \frac{\text{rise}}{\text{run}}. (The Greek letter delta, Δ, is commonly used in mathematics to mean "difference" or "change".) Given two points (x_1,y_1) and (x_2,y_2), the change in x from one to the other is x_2-x_1 (run), while the change in y is y_2-y_1 (rise). Substituting both quantities into the above equation generates the formula: :m = \frac{y_2 - y_1}{x_2 - x_1}. The formula fails for a vertical line, parallel to the y axis (see Division by zero), where the slope can be taken as infinite, so the slope of a vertical line is considered undefined. Examples Suppose a line runs through two points: P = (1, 2) and Q = (13, 8). By dividing the difference in y-coordinates by the difference in x-coordinates, one can obtain the slope of the line: :m = \frac{\Delta y}{\Delta x} = \frac{y_2 - y_1}{x_2 - x_1} = \frac{(8 - 2)}{(13 - 1)} = \frac{6}{12} = \frac{1}{2}. :Since the slope is positive, the direction of the line is increasing. Since |m| m = \frac{ 21 - 15}{3 - 4} = \frac{6}{-1} = -6. :Since the slope is negative, the direction of the line is decreasing. Since |m| > 1, this decline is fairly steep (decline > 45°). ==Algebra and geometry==

{{bulleted list : y = mx + b then m is the slope. This form of a line's equation is called the slope-intercept form, because b can be interpreted as the y-intercept of the line, that is, the y-coordinate where the line intersects the y-axis. : y - y_1 = m(x - x_1). : ax + by + c = 0 is : -\frac{a}{b}. : m = \tan(\theta) and : \theta = \arctan (m)   (this is the inverse function of tangent; see inverse trigonometric functions). }} Examples For example, consider a line running through points (2,8) and (3,20). This line has a slope, , of : \frac {(20 - 8)}{(3 - 2)} = 12. One can then write the line's equation, in point-slope form: : y - 8 = 12(x - 2) = 12x - 24. or: : y = 12x - 16. The angle θ between −90° and 90° that this line makes with the -axis is :\theta = \arctan(12) \approx 85.2^{\circ} . Consider the two lines: and . Both lines have slope . They are not the same line. So they are parallel lines. Consider the two lines and . The slope of the first line is . The slope of the second line is . The product of these two slopes is −1. So these two lines are perpendicular. == Statistics ==

In statistics, the gradient of the least-squares regression best-fitting line for a given sample of data may be written as: :m = \frac{rs_y}{s_x}, This quantity m is called as the regression slope for the line y=mx+c. The quantity r is Pearson's correlation coefficient, s_y is the standard deviation of the y-values and s_x is the standard deviation of the x-values. This may also be written as a ratio of covariances: :m = \frac{\operatorname{cov}(Y,X)}{\operatorname{cov}(X,X)} ==Calculus==

is the slope of a line that is tangent to the curve at that point. Note: the derivative at point A is positive where green and dash–dot, negative where red and dashed, and zero where black and solid. The concept of a slope is central to differential calculus. For non-linear functions, the rate of change varies along the curve. The derivative of the function at a point is the slope of the line tangent to the curve at the point and is thus equal to the rate of change of the function at that point. If we let Δx and Δy be the distances (along the x and y axes, respectively) between two points on a curve, then the slope given by the above definition, :m = \frac{\Delta y}{\Delta x}, is the slope of a secant line to the curve. For a line, the secant between any two points is the line itself, but this is not the case for any other type of curve. For example, the slope of the secant intersecting y = x2 at (0,0) and (3,9) is 3. (The slope of the tangent at is also 3 − a consequence of the mean value theorem.) By moving the two points closer together so that Δy and Δx decrease, the secant line more closely approximates a tangent line to the curve, and as such the slope of the secant approaches that of the tangent. Using differential calculus, we can determine the limit, or the value that Δy/Δx approaches as Δy and Δx get closer to zero; it follows that this limit is the exact slope of the tangent. If y is dependent on x, then it is sufficient to take the limit where only Δx approaches zero. Therefore, the slope of the tangent is the limit of Δy/Δx as Δx approaches zero, or dy/dx. We call this limit the derivative. :\frac{\mathrm dy}{\mathrm dx} = \lim_{\Delta x \to 0}\frac{\Delta y}{\Delta x} The value of the derivative at a specific point on the function provides us with the slope of the tangent at that precise location. For example, let y = x2. A point on this function is (−2,4). The derivative of this function is . So the slope of the line tangent to y at (−2,4) is . The equation of this tangent line is: or . ==Difference of slopes==

An extension of the idea of angle follows from the difference of slopes. Consider the shear mapping :(u,v) = (x,y) \begin{pmatrix}1 & v \\ 0 & 1 \end{pmatrix}. Then (1,0) is mapped to (1,v). The slope of (1,0) is zero and the slope of (1,v) is v. The shear mapping added a slope of v. For two points on \{(1,y):y\in\R\} with slopes m and n, the image :(1,y)\begin{pmatrix}1 & v \\ 0 & 1\end{pmatrix} = (1, y + v) has slope increased by v, but the difference n-m of slopes is the same before and after the shear. This invariance of slope differences makes slope an angular invariant measure, on a par with circular angle (invariant under rotation) and hyperbolic angle, with invariance group of squeeze mappings. == Slope (pitch) of a roof ==

The slope of a roof, traditionally and commonly called the roof pitch, in carpentry and architecture in the US is commonly described in terms of integer fractions of one foot (geometric tangent, rise over run), a legacy of British imperial measure. Other units are in use in other locales, with similar conventions. For details, see roof pitch. == Slope of a road or railway ==

There are two common ways to describe the steepness of a road or railroad. One is by the angle between 0° and 90° (in degrees), and the other is by the slope in a percentage. See also steep grade railway and rack railway. The formulae for converting a slope given as a percentage into an angle in degrees and vice versa are: : \text{angle} = \arctan \left( \frac{\text{slope}}{100\%} \right) (this is the inverse function of tangent; see trigonometry) and : \mbox{slope} = 100\% \times \tan( \mbox{angle}), where angle is in degrees and the trigonometric functions operate in degrees. For example, a slope of 100% or 1000‰ is an angle of 45°. A third way is to give one unit of rise in say 10, 20, 50 or 100 horizontal units, e.g. 1:10. 1:20, 1:50 or 1:100 (or "1 in 10", "1 in 20", etc.) 1:10 is steeper than 1:20. For example, steepness of 20% means 1:5 or an incline with angle 11.3°. Roads and railways have both longitudinal slopes and cross slopes. File:Nederlands verkeersbord J6.svg|Slope warning sign in the Netherlands File:PL road sign A-23.svg|Slope warning sign in Poland File: Skloník-klesání.jpg|A 1371-meter distance of a railroad with a 20‰ slope. Czech Republic File: Railway gradient post.jpg|Steam-age railway gradient post indicating a slope in both directions at Meols railway station, United Kingdom ==Other uses==

The concept of a slope or gradient is also used as a basis for developing other applications in mathematics: • Gradient descent, a first-order iterative optimization algorithm for finding the minimum of a function • Gradient theorem, theorem that a line integral through a gradient field can be evaluated by evaluating the original scalar field at the endpoints of the curve • Gradient method, an algorithm to solve problems with search directions defined by the gradient of the function at the current point • Conjugate gradient method, an algorithm for the numerical solution of particular systems of linear equations • Nonlinear conjugate gradient method, generalizes the conjugate gradient method to nonlinear optimization • Stochastic gradient descent, iterative method for optimizing a differentiable objective function ==See also==