In Mathematics, the concept of matrices has many applications. Matrices are useful to manipulate, represent the linear maps between finite-dimensional vector spaces. It helps to express the quadratic expressions, that allows studying the behaviour of the critical points. Some matrix properties are used in most of the theories in Maths. Apart from Mathematics, the matrices are also used in various disciplines like Engineering, Science, Business, Statistics, and so on. Let us discuss the uses of matrices in multiple fields in detail.

In Computer Graphics, the matrices are used to represent the object and to calculate the transformation of the object. The transformation of objects can be computed using **matrix multiplication**. The projection of three-dimensional objects on a two-dimensional screen uses a matrix to accomplish the task. The matrix can convert geometric data into different coordinate systems. The coefficients of the matrix represent the scale of a vector that undergoes during the transformation process. The rows and columns of the matrix represent the rows and columns of pixels, and the numerical entries represent the colour values of the pixels.

In Business, a matrix is an essential tool to calculate the maximum profit under a particular set of restrictions. Linear programming optimization is necessary for a business that uses the matrix to forecast business results. It helps to generate graphs and charts to shows their progress over a while. Whenever the number of input features increases, it is easy to show up in vector form, and there it uses matrices to determine the result.

In Statistics, the collection of data is expressed in terms of matrices. Much like an excel worksheet, a matrix is a rectangular array of numbers that are arranged in rows and columns. For statistical reports, various rules of matrix operations in linear algebra are used to get the output. In specific models, a statistician uses a design matrix, also known as the model matrix, where the data contains the set of independent variables.

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