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Analyzing Variability
Published in Erick C. Jones, Supply Chain Engineering and Logistics Handbook, 2020
Logistic regression is used to provide information about analysis of the two values of interest, that is, good or bad, vote or not vote, pass or fail, enlist or not enlist, yes or no. Since a logistic regression model takes only the value of 0 and 1, it is also known as binary regression model. The general equation for logistic regression is given as y=b0+b1x1+e
Saving Money with Six Sigma Projects
Published in Kim H. Pries, Jon M. Quigley, Reducing Process Costs with Lean, Six Sigma, and Value Engineering Techniques, 2012
Additionally, we have other forms of regression; for example, logit, probit, binary regression, and others. Some of these are relatively exotic for use in Six Sigma projects. In general, the more peculiar regressions find use in situations where we are assessing medical or biological data.
A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets
Published in Cogent Engineering, 2023
Hasti Samadi, Mohammed Ahsan Kollathodi
Logistic regression is a statistical model in its fundamental form which would utilize a logistic function to model a binary dependent variable. In regression analysis, logistic regression would approximate the parameters of a logistic model which is a type of binary regression. Mathematically a binary logistic model has a dependent variable with two possible values, such as on or off which is represented by an indicator variable, where the two values are labelled as “0” and “1”. It’s an addition to the linear regression model for classification problems. In machine learning, logistic regression is a supervised learning classification problem that is utilized to predict the probability of a target function. This kind of target or dependent variable is dichotomous which would mean that there would be only two feasible classes in the output. In general, logistic regression would infer binary logistic regression possessing binary target variables. In such a type of classification, a dependent variable will have only two possible categories that can take a value of 0 or 1 (Cheng et al., 2010; DiMaio et al., 2011; Ismail et al., 2016; Krantiwadmare,2021; Samadi, 2011).
PersonalisedComfort: a personalised thermal comfort model to predict thermal sensation votes for smart building residents
Published in Enterprise Information Systems, 2022
Saif Ur Rehman, Abdul Rehman Javed, Mohib Ullah Khan, Mubashar Nazar Awan, Adees Farukh, Aseel Hussien
Logistic regression is a mathematical method which fundamentally uses logistic function to create a binary outcome variable model. In terms of regression, logistic regression (Javed et al. 2020b; Tolles and Meurer 2016) estimates the arguments of a logistic model which is a form of binary regression. Thus, it’s outcomes are in binary state. Either 1 or 0.