Mean Squared Error (MSE) : Mean squared error (MSE) is a widely used loss function in machine learning, which is often used to evaluate the performance of a model on a given dataset. MSE is defined as the average squared difference between the predicted values and the true values. In other words, it is the […]

Mean-range plot : A mean-range plot is a type of statistical chart that shows the relationship between the mean and range of a set of data. The mean is the average of all the data points, and the range is the difference between the highest and lowest values in the data set. For example, let’s […]

Mean Deviation : Mean deviation is a statistical measure that is used to describe the dispersion of a dataset. It is calculated by taking the absolute difference between each data point and the mean of the data, and then taking the average of those differences. For example, let’s say we have a dataset of 5 […]

Mean Absolute Error (MAE) : Mean Absolute Error (MAE) is a measure of prediction accuracy in regression analysis. It is calculated by taking the average of the absolute differences between the predicted values and the actual values. For example, if a model predicts the values 3, 4, 5, and 6 for a set of actual […]

Mean and dispersion additive model (MADAM) : The Mean and Dispersion Additive Model (MADAM) is a statistical model that is used to analyze the relationship between a response variable and one or more predictor variables. It is a type of generalized linear model that is commonly used in many areas of research, including psychology, sociology, […]

Mean : Mean is a term used in statistics to describe the central tendency of a set of data. It is also known as the average and is calculated by adding up all the values in a data set and dividing by the number of values in the set. One example of mean can be […]

McCabe-Tremayne test : The McCabe-Tremayne test is a statistical test used to determine whether a time series is stationary or non-stationary. A time series is a sequence of data points measured at successive time intervals. Stationarity refers to the statistical properties of a time series, such as the mean and variance, which do not change […]

Maximum Likelihood Estimation : Maximum likelihood estimation (MLE) is a statistical technique used to estimate the values of parameters in a given model. It is based on the idea that the observed data is most likely to have occurred under the model with the highest probability. To understand MLE, let’s consider two examples. Example 1: […]

Maximum a posteriori estimate (MAP) : Maximum a posteriori (MAP) estimate is a statistical technique used to estimate the probability of an event or value of a parameter given some prior information. This method is often used in machine learning and data analysis, where it helps to make more informed predictions or decisions based on […]

Mauchly test : The Mauchly test is a statistical test used to determine if the assumption of sphericity has been violated in a repeated measures analysis of variance (ANOVA). This assumption states that the variances of the differences between all pairs of levels of the within-subjects factor are equal. Violation of this assumption can result […]