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Comparative research is a research methodology in the social sciences exemplified in cross-cultural or comparative studies that aims to make comparisons across different countries or cultures. A major problem in comparative research is that the data sets in different countries may define categories differently (for example by using different ...
In statistics and econometrics, cross-sectional data is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at a single point or period of time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects, typically with no regard to differences in time.
Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels
The hypothesis that a data set in a regression analysis follows the simpler of two proposed linear models that are nested within each other. Multiple-comparison testing is conducted using needed data in already completed F-test, if F-test leads to rejection of null hypothesis and the factor under study has an impact on the dependent variable. [1]
A database of biomedical and life sciences literature with access to full-text research articles and citations. [56] Includes text-mining tools and links to external molecular and medical data sets. A partner in PMC International. [57] Free EMBL-EBI [58] FSTA – Food Science and Technology Abstracts: Food science, food technology, nutrition
The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Thus, the input to QCA is a data set of any size, from small-N to large-N, and the output of QCA is a set of descriptive inferences or implications the data supports. In QCA's next step, inferential logic or Boolean algebra is used to simplify or reduce the number of inferences to the minimum set of inferences supported by the data.