Regression analysis is a statistical method for analyzing a relationship between two or more variables in such a manner that one variable can be predicted or explained by using information on the others.

Regression analysis is the oldest, and probably, most widely used multivariate technique in the social sciences. Unlike the preceding methods, regression is an example of dependence analysis in which the variables are not treated symmetrically. In regression analysis, the object is to obtain a prediction of one variable, given the values of the ...

Oct 1, 2014 · Braun et al. [11] employ multiple regression analysis to investigate timer settings, night cover effectiveness together with indoor and outdoor temperature and humidity on the electricity consumption of a supermarket. The more complex principal component analysis is used by Lam et al. in [12] for office buildings. This technique allows the same ...

Apr 1, 2008 · Regression analysis instead is used to describe the linear dependence of the outcome variable (or dependent variable) from one (or more) predictor variable (or independent variable). In a recent paper, 1 the relationship between serum albumin and free triiodothyronine (plasma levels of fT3) was investigated in a sample of 41 patients on chronic ...

May 1, 2022 · Survival analysis, also called a time-to-event analysis, can provide long-term information that can be used to predict outcomes in the case of some diseases. It is widely used to analyze data in which the time to the event is of interest (e.g., heart attack, stroke, and death), and such a response variable is often referred to as survival time ...

Binary Logistic Regression is defined as a type of regression analysis used when the dependent variable is binary, meaning it has two categories. It is commonly used when the outcome is coded as "1" or "0" and is not suitable for regular linear regression models.

Feb 1, 2024 · The evaluated linear regression analysis results in a scatter plot that resembles one from the early stages. After that, the correlation coefficient r will be determined, and its significance will be examined. Given a value for the independent variable, X, a regression line can be used to predict the value of the dependent variable, Y.

Jan 1, 2000 · 6. MULTIPLE REGRESSION ANALYSIS I S3 Prediction uses the regression weights obtained in a sample to predict the criterion in the population (or in other samples drawn from the popula- tion) in which the criterion scores are unknown (Darlington, 1990; Raju, Bilgic, Edwards, & Fleer, 1997).

Jan 1, 2019 · In this paper, we study functional regression with a random response curve and vector covariates. We propose a supervised least squares estimation procedure after utilizing B-spline functions to approximate the unknown functions and establish the asymptotic normality of the proposed estimators.

Oct 1, 2024 · The simplest regression analysis is undoubtedly one-variable, i.e., unary linear regression, and its function graph is simply a regression line: Eq.(1) h θ = θ 0 + θ 1 x where θ 0 is the intercept of the regression line, θ 1 is the slope of the regression line, h 0 is the dependent variable, and x is the independent variable.