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Missing data. In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. Missing data can occur because of nonresponse: no information is provided for one or more ...
Imputation (statistics) In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as " unit imputation "; when substituting for a component of a data point, it is known as " item imputation ". There are three main problems that missing data causes: missing data ...
Inverse probability weighting. Inverse probability weighting is a statistical technique for estimating quantities related to a population other than the one from which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. [1]
Listwise deletion is also problematic when the reason for missing data may not be random (i.e., questions in questionnaires aiming to extract sensitive information. [3] Due to the method, much of the subjects' data will be excluded from analysis, leaving a bias in data findings. For instance, a questionnaire may include questions about ...
The Wilcoxon signed-rank test is a nonparametric test of nonindependent data from only two groups. The Skillings–Mack test is a general Friedman-type statistic that can be used in almost any block design with an arbitrary missing-data structure. The Wittkowski test is a general Friedman-Type statistics similar to Skillings-Mack test. When the ...
Censoring (statistics) In statistics, censoring is a condition in which the value of a measurement or observation is only partially known. For example, suppose a study is conducted to measure the impact of a drug on mortality rate. In such a study, it may be known that an individual's age at death is at least 75 years (but may be more).
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis. Tukey's championing of EDA encouraged the development of statistical computing packages, especially S ...
Generalized estimating equation. In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different timepoints. [1][2] Although some believe that GEEs are robust in everything, even with the wrong choice of working ...