The distance formula, derived from the Pythagorean theorem, calculates the distance between two points in a plane using their coordinates. It is expressed as √((x2 - x1)² + (y2 - y1)²), where (x1, y1) and (x2, y2) represent the points. Mastering this formula is crucial for success in geometry and coordinate algebra.
Millions of flashcards designed to help you ace your studies
Sign up for freeWhat is the adjusted distance formula in four-dimensional space?
Where does the distance formula originate from?
How do you compute the distance between points \(C = (6, 8)\) and \(D = (3, 4)\) in a Cartesian plane?
Which step is necessary to correctly find the Euclidean distance between two points in a plane?
Which one of these is not a real-world application of the distance formula?
What theorem does the distance formula derive its principles from?
What is the formula for the distance between two points \(x_1, y_1\) and \(x_2, y_2\) in a 2D plane?
What is the distance between the points A = (3, 4) and B = (7, 1)?
What theorem is used to derive the Euclidean distance formula in two-dimensional space?
How is the Euclidean distance calculated in a three-dimensional space?
How is the distance between points \(A = (2, 3)\) and \(B = (5, 7)\) calculated in 2D space?
What is the adjusted distance formula in four-dimensional space?
Where does the distance formula originate from?
How do you compute the distance between points \(C = (6, 8)\) and \(D = (3, 4)\) in a Cartesian plane?
Which step is necessary to correctly find the Euclidean distance between two points in a plane?
Which one of these is not a real-world application of the distance formula?
What theorem does the distance formula derive its principles from?
What is the formula for the distance between two points \(x_1, y_1\) and \(x_2, y_2\) in a 2D plane?
What is the distance between the points A = (3, 4) and B = (7, 1)?
What theorem is used to derive the Euclidean distance formula in two-dimensional space?
How is the Euclidean distance calculated in a three-dimensional space?
How is the distance between points \(A = (2, 3)\) and \(B = (5, 7)\) calculated in 2D space?
Achieve better grades quicker with Premium
Geld-zurück-Garantie, wenn du durch die Prüfung fällst
Review generated flashcards
Start learning or create your own AI flashcards
Team Distance formula Teachers
The distance formula is a crucial tool in analytic geometry, enabling you to calculate the distance between two points in a two-dimensional or three-dimensional space, given their Cartesian coordinates. It is fundamental for various mathematical computations and real-world applications.
The distance formula derives its principles from the Pythagorean theorem. When you think of the distance between two points, you can visualise it as the hypotenuse of a right triangle formed by these points, thus connecting them geometrically through their coordinates.The formula for the distance between two points \(x_1, y_1\) and \(x_2, y_2\) in a two-dimensional plane is:\[d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}\]
Distance Formula: The formula is given by \[d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}\], where \(d\) is the distance between points \(x_1, y_1\) and \(x_2, y_2\).
Consider two points \(A = (3, 4)\) and \(B = (7, 1)\). The distance \(d\) between these points is calculated as follows:
Remember that the distance formula can be used in three dimensions by including the z-coordinates, transforming the formula to \[d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2 + (z_2 - z_1)^2}\].
The Euclidean distance formula is a cornerstone in geometry. It allows you to find the shortest path between two points in a plane or space, given their coordinates.
The formula for Euclidean distance in a two-dimensional space can be derived using the Pythagorean Theorem. For points \(A = (x_1, y_1)\) and \(B = (x_2, y_2)\), the horizontal and vertical distances between the points form the two legs of a right triangle, with the distance \(d\) being the hypotenuse.
In three dimensions, the Euclidean distance formula extends to include the z-coordinates as well. For points \(A = (x_1, y_1, z_1)\) and \(B = (x_2, y_2, z_2)\), the distance \(d\) is calculated as follows:\[d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2 + (z_2 - z_1)^2}\]
Consider two points, \(A = (1, 2)\) and \(B = (4, 6)\).Using the Euclidean distance formula:
Double-check your calculations to avoid common mistakes such as sign errors when subtracting coordinates.
The Euclidean distance formula is widely used in various fields:
Understanding how to apply the distance formula through examples can make the concept clearer and more intuitive. Let's delve into some practical scenarios to enhance your grasp of this important mathematical tool.
Consider points \(A = (2, 3)\) and \(B = (5, 7)\). The distance \(d\) is determined as follows:
In higher dimensions, the distance formula adapts to include additional coordinates. For instance, in four-dimensional space for points \(A = (x_1, y_1, z_1, w_1)\) and \(B = (x_2, y_2, z_2, w_2)\), the formula becomes:\[d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2 + (z_2 - z_1)^2 + (w_2 - w_1)^2}\]. This adjustment allows for the computation of distances in even more complex spaces.
Imagine you are calculating the distance between two points on a graph that represents a real-world scenario. Let's use the distance formula with specific coordinates to illustrate this.
You have two locations on a map, represented by the coordinates \(A = (1, 2)\) and \(B = (4, 6)\). To determine the distance between these points:
While using the distance formula, ensure you square the differences, not the coordinates themselves.
The distance formula is frequently applied in Cartesian planes to find the shortest path between two points. This method is not just theoretical—it’s widely applicable in various practical fields.
Consider an example where two points are given: \(C = (6, 8)\) and \(D = (3, 4)\). You can find the distance \(d\) as follows:
Consider Cartesian planes where axes aren't perpendicular, such as in certain trigonometric calculations or when non-Euclidean geometry is involved. The basic concepts of distance remain valid, but these alternate geometries require adaptations of the formula to accommodate curved spaces or different metrics.
The distance formula is extensively used in various branches of mathematics, especially in analytic geometry, to find the distance between two points in a plane or space. Understanding this formula is essential for tackling numerous geometric problems and real-world scenarios.
The distance formula is derived from the Pythagorean theorem. In a two-dimensional Cartesian plane, it calculates the distance between two points, helping you visualise their relative positions on the plane.The formula for the distance between two points \((x_1, y_1)\) and \((x_2, y_2)\) is:\[d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}\]
Take points \(P = (2, 3)\) and \(Q = (5, 7)\). The distance \(d\) between these points can be calculated as follows:
The distance formula originates from the Pythagorean theorem, which is applicable in right-angled triangles. By considering the distance between two points as the hypotenuse of a right-angled triangle, you can simplify the computation.For two points, \((x_1, y_1)\) and \((x_2, y_2)\) in a plane, the horizontal distance \((x_2 - x_1)\) and the vertical distance \((y_2 - y_1)\) form the two legs of the right triangle.
Using the Pythagorean theorem:\[a^2 + b^2 = c^2\]where \(a\) and \(b\) are the legs of the triangle, and \(c\) is the hypotenuse.Applying to our coordinates:\[c = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}\]This is how the distance formula is derived.
In three dimensions, the distance formula incorporates the z-coordinates, adding another term in the square root.
In coordinate geometry, the distance formula is vital for calculating the distance between points in a Cartesian plane. It simplifies the process of determining the length of line segments and the spatial relationships between points.
To find the distance between points \((3, 4)\) and \((7, 1)\) within a coordinate plane:
In coordinate geometry, understanding the distance formula aids in solving more complex geometric problems, such as finding the equation of a circle, determining midpoints, and solving for areas of polygons. The formula remains effective across different dimensions and coordinates.
The distance formula is not confined to theoretical mathematics; it has practical applications across various fields. Here are some real-world scenarios where the distance formula plays a crucial role.
Navigation: Used in GPS technology to calculate the shortest path between two geographical locations.
In navigation, the distance formula helps in determining routes. For instance, calculating the shortest distance between two cities based on their coordinates to provide efficient travel routes.
Engineering and Physics: Utilised in calculating distances between objects, structural analysis, and motion paths.
In engineering, the distance formula is crucial for ensuring the proper alignment and placement of structures. In physics, it's essential for calculating the trajectory of particles, analysing forces, and understanding spatial relationships in various contexts.
Computer Science: Essential in algorithms such as K-means clustering, image processing, and network topology analysis.
The distance formula is adaptable to higher dimensions, meaning it can be used in three-dimensional space for even more versatile applications.
Sign up for free to gain access to all our flashcards.
At StudySmarter, we have created a learning platform that serves millions of students. Meet the people who work hard to deliver fact based content as well as making sure it is verified.
Lily Hulatt is a Digital Content Specialist with over three years of experience in content strategy and curriculum design. She gained her PhD in English Literature from Durham University in 2022, taught in Durham University’s English Studies Department, and has contributed to a number of publications. Lily specialises in English Literature, English Language, History, and Philosophy.
Get to know Lily
Gabriel Freitas is an AI Engineer with a solid experience in software development, machine learning algorithms, and generative AI, including large language models’ (LLMs) applications. Graduated in Electrical Engineering at the University of São Paulo, he is currently pursuing an MSc in Computer Engineering at the University of Campinas, specializing in machine learning topics. Gabriel has a strong background in software engineering and has worked on projects involving computer vision, embedded AI, and LLM applications.
Get to know Gabriel