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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 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.
11.9.1 Simple Linear Regression. Simple linear regression analysis comprises the study of the association between a continuous outcome variable and a continuous covariate. The relationship is assumed to be linear, i.e., a straight line in the slope-intercept form, where x is the covariate and y the outcome: y = β 0 + β 1 x.
Nov 1, 2019 · To relax these rigidities, numerous researchers have modified and extended concepts of statistical regression analysis by means of concepts of fuzzy set theory. By now, there is a large number of papers on the topic of fuzzy regression analysis, especially concerning possibilistic, fuzzy least squares or machine learning approaches.
The Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly theoretical developments of multivariate statistics combined with innovative applications pertaining to the analysis and interpretation of multidimensional data. …. View full aims & scope.
MULTIPLE REGRESSION ANALYSIS Inadequate results are sometimes obtained with a single independent variable. This shows that one independent variable does not provide enough information to predict the corresponding value of the dependent variable. We can approach this problem, if we use additional independent variables and develop a multiple regression analysis to achieve a meaningful relationship. Here, we can employ a linear regression model in cases where the dependent variable is affected ...
May 1, 2022 · The survival probability of a population at any given time is often of interest. Kaplan and Meier proposed a nonparametric method to estimate the survival probability that is the most commonly used method. 2 Define T as the response variable, time to event of interest, or survival time. The survival function is defined as S t = P an individual ...
Apr 1, 2021 · Abstract Regression analysis constitutes an important tool for investigating the effect of explanatory variables on response variables. When outliers and bias errors are present, the weighted least squares estimator can perform poorly. For this reason, alternative robust techniques have been studied in several areas of science. However, often these different scientific communities are disconnected from each other, culminating in the scarcity of knowledge exchange among these areas. Thus ...
Apr 25, 2023 · A review of outlier robust estimation methods for nonparametric regression models is provided, paying particular attention to practical considerations. Since outliers can also influence negatively the regression estimator by affecting the selection of bandwidths or smoothing parameters, a discussion of robust alternatives for this task is also ...
Dec 1, 2014 · This paper surveys the literature which examines the effect of education on economic growth. Specifically, we apply meta-regression analysis to 57 studies with 989 estimates and show that there is substantial publication selection bias toward a positive impact of education on growth.
