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Detecting and resolving errors in coding and data entry.
Replacing missing values in data analysis by estimating values from the available data.
The numerical difference between an observed value and the value predicted by the regression line.
The value or category in a distribution with the highest frequency.
The middle value in a distribution.
Graphic depiction of a bivariate distribution.
Indicates how much the dependent variable changes for every one-unit increase in the independent variable.
Examples are Cramer’s phi and the correlation coefficient.
Consists of editing, coding, data entry, and data cleaning.
The most commonly used statistical measure of variation.
Shows whether the association in a contingency table is statistically significant.
A graphic display of a univariate distribution.