In this article, we will learn how to compare two cell arrays of strings of different sizes in MATLAB. Cell Array of Strings In MATLAB, a cell array of strings is a data structure that can store different types of elements. The element of a cell array are enclosed within curly braces `{}`. Syntax We can use the following syntax to create a cell array of strings, A = {'string1', 'string2', 'string3', …'stringN'}; Here, `A` is a cell array of strings that contains N string elements namely, `string1`, `string2`, …, `stringN`. In MATLAB, the cell arrays of strings are ... Read More

In an image, a boundary is an element that separates an object from the background of the image or two different regions within an image. Boundary provides information about the shape and structure of objects present in the image. Boundaries are mainly classified into the following two types: Inner Boundary It is the boundary of an object within an image that separates it from the image background. The inner boundary is basically the outline of the object and provides information about the shape of the object. Therefore, by extracting the inner boundary of an object, we can identify and analyze ... Read More

An optical or visual phenomenon that creates the perception of distortion, movement or any other type of fascinating visual effect by using back and white patterns is referred to as back and white optical illusion. These back and white patterns utilize the way our eyes and brain process the visual information and create an illusion to mislead our perception. The back and white optical illusions use various black and white patterns, shapes, and lines arranged in a specific style. These arrangements are made such that they can create illusion of movement, distortion, depth, and more. In this article, we will ... Read More

Alteryx is a user-friendly Data analytics platform. It is a robust data analytics and processing platform that enables users to extract, transform and process data from multiple sources and perform complex computation and analysis using a drag-and-drop interface. The reason behind the tool’s wide usage and fame is its no-code implementation of data preparation and analysis which streamlines business analysis in corporates. Getting Started with Alteryx Alteryx Designer is used for creating workflows for analyzing, blending data, and performing advanced analytics (such as predictive, spatial, and prescriptive) using the drag-and-drop user interface. A workflow in Alteryx consists of connected tools ... Read More

In MATLAB, for removing NaN value from a matrix, the following two functions are most widely used. remmissing () isnan () Now let us understand the removal of NaN value from a matrix in MATLAB using these two functions with the help of example programs. Removing NaN Values Using the isnan () Function In MATLAB, we can use the "isnan()" function to find the NaN values in a matrix and then can be used to remove them using logical indexing. The "isnan ()" function returns a value TRUE (logic 1) for NaN elements and FALSE (logic 0) for ... Read More

MATLAB is a programming environment developed for scientists and engineers to design and analyze system, perform data analysis, create visualizations, and more. MATLAB stands for Matrix Laboratory, it is a programming and interactive platform developed by MathWorks to provide a variety of tools for programming mathematical functions and operations, data analysis, etc. MATLAB is extensively used in different field of science, engineering, finance, economics, and more. Read this article to understand the process of finding the exact string in a cell array in MATLAB. What is a Cell Array in MATLAB? In MATLAB, a cell array is a data structure ... Read More

MATLAB is a programming environment developed for scientists and engineers to design and analyze system, perform data analysis, create visualizations, and more. MATLAB stands for Matrix Laboratory, it is a programming and interactive platform developed by MathWorks to provide a variety of tools for programming mathematical functions and operations, data analysis, etc. MATLAB is extensively used in different field of science, engineering, finance, economics, and more. Read this tutorial to learn the different methods of finding the closest value in an array in MATLAB. But before that let's get an idea about Array in MATLAB. What is an Array in ... Read More

MATLAB is the acronym for Matrix Laboratory. MATLAB is a programming environment developed for scientists and engineers to design and analyze systems. MATLAB platform uses its MATLAB programming language which is a matrix-based language. MATLAB language allows users to write expression of computational mathematics in natural way. With the help of MATLAB, a user can analyze data, develop algorithms, design system models and applications, and more. Therefore, MATLAB is one of the most popular design and analysis tools used by engineers and scientists globally. It finds applications in several different fields of science and technology, including deep learning, machine learning, ... Read More

Introduction A popular statistical method for comprehending and simulating the connections between variables is regression analysis. The dependent variable is frequently assumed to have a normal distribution, though. The accuracy and dependability of the regression model may be jeopardized if this assumption is broken. The Box−Cox transformation offers a potent method for changing skewed or non−normal dependent variables to resemble a normal distribution in order to overcome this issue. We shall examine the Box−Cox transformation theory and use it in regression models in this post. We'll look at the transformation's justification and how it helps to satisfy the ... Read More

Introduction Multicollinearity, a phenomenon characterized by high correlation or linear dependence between predictor variables, poses significant challenges in regression analysis. This article explores the detrimental effects of multicollinearity on statistical models, focusing on issues such as unreliable coefficient estimates, reduced model interpretability, increased standard errors, and inefficient use of variables. We delve into the consequences of multicollinearity and discuss potential solutions to mitigate its impact. By understanding and addressing multicollinearity, researchers, and practitioners can improve the accuracy, reliability, and interpretability of regression models, enabling more robust analysis and informed decision−making. Problems with Multi−Collinearity Unreliable coefficient estimates Because ... Read More