**a mathematical technique that determines the best way to use available resources**. Managers use the process to help make decisions about the most efficient use of limited resources – like money, time, materials, and machinery.

What is the role of listening in communication?

**what is listening in communication**.

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Production Management: LP is applied **for determining the optimal allocation of such resources as materials, machines, manpower, etc.** **by a firm**. It is used to determine the optimal product- mix of the firm to maximize its revenue. It is also used for product smoothing and assembly line balancing.

Companies like **Amazon and FedEx** use linear programming to find the shortest and most efficient delivery routes. Linear programming is also used in machine learning applications where a neural network is trained to fit model of a function in order to label input data and predict unknown future values.

The Linear Programming Problems (LPP) is a problem that is **concerned with finding the optimal value of the given linear function**. The optimal value can be either maximum value or minimum value. Here, the given linear function is considered an objective function.

LP **makes logical thinking and provides better insight into business problems**. Manager can select the best solution with the help of LP by evaluating the cost and profit of various alternatives. LP provides an information base for optimum allocation of scarce resources.

Linear programming is **used to obtain optimal solutions for operations research**. Using linear programming allows researchers to find the best, most economical solution to a problem within all of its limitations, or constraints. Many fields use linear programming techniques to make their processes more efficient.

linear programming, **mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints**. This technique has been useful for guiding quantitative decisions in business planning, in industrial engineering, and—to a lesser extent—in the social and physical sciences.

The most classic example of a linear programming problem is **related to a company that must allocate its time and money to creating two different products**. The products require different amounts of time and money, which are typically restricted resources, and they sell for different prices.

Linear Programming Problems in maths is **a system process of finding a maximum or minimum value of any variable in a function**, it is also known by the name of optimization problem. … The problem is generally given in a linear function which needs to be optimized subject to a set of different constraints.

Answer: The characteristics of linear programming are: **objective function, constraints, non-negativity, linearity, and finiteness**.

Linear programming is **a recently devised technique for providing specific numerical solutions of problems** which earlier could be solved only in vague qualitative terms by using the apparatus of the general theory of the firm. … It is a specific approach within the general framework of economic theory.

The greatest advantage of the linear model of communication is that **the message is clear and unambiguous**, leaving the audience with little or no ability to change the message content, style, or presentation.

The assumption of linear programming are: **The relation shown by the constraints and the objective function are linear**. The parameters could vary as per magnitude. The basic characteristics of linear programming is to find the optimal value based on certain available problem.

Linear models are often **useful approximations to nonlinear relationships** as long as we restrict our attention to realistic and relatively modest variations in the variables. … If variables are related to each other by a power function, then there is a log-linear relationship between them.

Strengths: Linear regression is straightforward to understand and explain, and can be regularized to avoid overfitting. In addition, linear models can be updated easily with new data using stochastic gradient descent. Weaknesses: **Linear regression performs poorly when there are non-linear relationships**.

Why linear regression is important Linear-regression models are **relatively simple and provide an easy-to-interpret mathematical formula that can generate predictions**. Linear regression can be applied to various areas in business and academic study.

The Linear Model of Communication is a **model that suggests communication moves only in one direction**. The Sender encodes a Message, then uses a certain Channel (verbal/nonverbal communication) to send it to a Receiver who decodes (interprets) the message. … A receiver is the recipient of a message.

Linear regression is the next step up after correlation. It is used **when we want to predict the value of a variable based on the value of another variable**. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable).