High Dimensional Data : High dimensional data refers to data that has a large number of features or variables. For example, a dataset with 100 columns or features would be considered high dimensional. This is in contrast to low dimensional data, which has only a few features. One example of high dimensional data is a […]

High Breakdown Methods : In robust statistics, high breakdown methods are statistical methods that have a high breakdown point, which is the maximum fraction of outliers that the method can handle before it becomes substantially less effective. This means that these methods are resistant to the effects of outliers, which are data points that are […]

Hidden Time Effects : Hidden time effects in data are factors that can affect the relationship between variables in a study over time. These factors can be difficult to identify and account for, and as a result, they can potentially lead to incorrect conclusions being drawn from the data. One example of a hidden time […]

Hidden Markov Models : A hidden Markov model (HMM) is a statistical model that allows us to predict the likelihood of certain sequences of observations, given a set of underlying hidden states. This can be useful in many applications, such as speech recognition, financial forecasting, and biological sequence analysis. To understand how HMMs work, let’s […]

Heteroscedasticity : Heteroscedasticity is a statistical term that refers to the unequal dispersion of the residuals in a regression model. In other words, it is a situation where the variability of the residuals (the errors or differences between the observed and predicted values) is not constant across all values of the independent variable. One example […]

Heterogeneous : Heterogeneous refers to the presence of different types or forms within a single group or system. This means that the individuals or components within the group are not all the same and possess distinct characteristics or qualities. One example of heterogeneous can be seen in a classroom setting. Each student in a classroom […]

Heywood Cases : Heywood cases occur in factor analysis when there is a variable with only one observed value. This can lead to problems in the factor analysis because it causes a singularity in the matrix and can result in incorrect factor loadings. One example of a Heywood case is when there is a variable […]

Heuristic Computer Program : A heuristic computer program is a type of algorithm that uses practical, approximate, or educated guesses to solve problems. These programs are designed to provide quick solutions to complex problems, even if the solutions may not be the most accurate or optimal. One example of a heuristic program is a spell […]

Helmert Contrast : Helmert contrast is a statistical technique used to compare the means of two or more groups. It is used to determine whether the differences between the means are statistically significant, or if they are due to chance. One example of using Helmert contrast is in a study of the effectiveness of a […]

Hat Matrix : The Hat Matrix, also known as the Leverage Matrix or Influence Matrix, is a matrix that describes the relationship between the dependent variable in a regression model and the individual observations in the dataset. It is called the Hat Matrix because it resembles the letter “hat” (^) in algebraic notation. In a […]