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Missing data is one of the most common problems in almost all statistical analyses. If the data is not available for all the observations of variables in the model, then it is a case of ‘missing data’.
estimation in supervised learning, Supervised learning, trend analysisBootstrap and jackknife are superficially similar statistical techniques that involve re-sampling the data. They are nonparametric and specific resampling techniques that can estimate standard errors and confidence intervals of a population parameter.
estimation in supervised learning, Supervised learningChallenges of bibliometrics arise at every step of the study, selection of the specific software and type of analyses done. Thus, this article presented both the challenges and the solutions occurring in data analysis of bibliometrics.
BibliometricsNeural network, popularly known as Artificial Neural Network (ANN) is an information processing system with a large number of nodes and connections as part of a structure which helps in processing complex information.
regressions in supervised learning, Supervised learning, trend analysisUntil recently, Karl Pearson Correlation analysis was one of the most popular methods to measure linear association between two or more than two variables in a data set. For example, establishing the Karl Pearson Correlation between X variable and Y variable, where both variables belong to a single data set. Canonical Correlation Analysis (CCA), on the other hand, helps measure the correlation among variables which are in different datasets.
correlation, correlation in supervised learning, Supervised learningA decision tree is a graphical representation of possible solutions to a problem based on given conditions. It is called a tree because diagrammatically it starts with a single box (target variable) and ends up in numerous branches and roots (numerous solutions).
classification in supervised learning, Supervised learning, trend discoveryThus to assess the model, a common practice in data science is to iterate over various models and select the most appropriate model. In other words it is important to test the same model with different values of parameters.This is called the cross validation method.
estimation in supervised learning, Supervised learningThe Comprehensive Meta-analysis (CMA) software is a user-friendly and diverse software. It is capable of handling and executing multiple tests involved in performing Meta-analysis.
CMA, CMA introduction