Eberhardt’s statistic is a metric used to measure the effectiveness of a marketing campaign. It was first introduced in 1997 by Professor Jeffrey Eberhardt, who believed that traditional methods of measuring success, such as return on investment (ROI) and cost per exposure (CPE), failed to capture the full impact of marketing efforts. To address this […]
Dual system estimates are a type of cost-benefit analysis used to assess the value of different decision options by comparing their costs and benefits. Two Systems in Dual System Estimates It involves two separate systems: the financial system, which focuses on monetary costs and benefits, and the non-financial system, which considers social, environmental, legal and […]
Dropout is a regularization technique used in machine learning and deep learning to reduce overfitting. It works by randomly dropping neurons from the neural network during training, which forces the model to learn more generalizable representations of the data and reduces the likelihood of memorizing data. This is especially useful when training deep neural networks […]
Dispersion, also known as variance, scatter or dispersion, is a measure of the spread of a dataset around its mean or average. It is a statistical measure that describes how much the individual elements in a given dataset vary from the average value. In other words, it is a measure of variability within a dataset. […]
Discriminant analysis is a predictive modelling technique used to identify relationships between variables and the classifications of outcomes. It is often applied in cases where there is a need to differentiate among two or more groups of observations, for example in determining whether a customer will respond positively or negatively to a marketing campaign; or, […]
Disclosure risk is a type of security risk that poses a threat to organizations, businesses, and individuals that involves the unauthorized disclosure of sensitive information. It can be caused by either an intentional or unintentional action. Disclosure risk can arise from the transfer of confidential data outside of the organization or to other parties, through […]
Design-based inference is a type of statistical inference which involves using experimental design principles to draw conclusions from observational data. It enables researchers to develop and test hypotheses about the population based on the data produced in their experiments. Uses of Design-Based Inference Design-based inference can be used to investigate the influence of different factors […]
A Critical Region, or CR, is a section of a statistical analysis where the null hypothesis is either accepted or rejected. The null hypothesis is a statement which states that there is no relationship between two variables, such as in an experiment. Uses of Critical Region It can also be used for a comparison to […]
Concentration matrix, also known as the Scatter Matrix or Dispersion Matrix, is an inverse of the variance. It is used to measure the dispersion and variability of a given set of data points. Estimation of Each Point It provides an estimate of how different each point in a dataset is from one another, with higher […]
A Composite Hypothesis is a statistical hypothesis that proposes a single value for an unknown parameter, such as the population mean. This type of hypothesis typically involves taking multiple samples from a population and combining them to form a single composite estimate. The Aim of Composite Hypothesis The estimation process may also involve making assumptions […]
