Box-Muller Transformation : The Box-Muller Transformation is a method used to generate normally distributed random numbers. This is useful in many statistical and mathematical applications, as many real-world phenomena follow a normal distribution. To understand the Box-Muller Transformation, let’s first consider the concept of uniformly distributed random numbers. These are numbers that are generated randomly […]

Box-Cox Transformation : The Box-Cox transformation is a statistical method that is used to transform data that is non-normal into a more normal distribution. This transformation is typically used when data is skewed, which can make it difficult to analyze and interpret. By applying a Box-Cox transformation, data can be made more normal, which allows […]

Box Counting Method : Box counting is a mathematical method used to calculate the fractal dimension of a geometric object. It involves dividing the object into smaller and smaller boxes and counting the number of boxes that contain at least a part of the object. The fractal dimension is then calculated using a mathematical formula […]

Box and Whisker plot : A box and whisker plot is a graphical representation of a dataset that displays the distribution of the data and its range. It consists of a box, which represents the middle 50% of the data, and two whiskers, which extend from the box to show the range of the data. […]

Boundary Estimation : Boundary estimation is a statistical method that involves determining the range within which the true value of a population parameter is likely to lie. This range is determined by using sample data to calculate an interval estimate, which is a type of confidence interval. For example, suppose a researcher wants to estimate […]

Bootstrap : Bootstrap is a statistical method used to estimate the sampling distribution of a statistic through the use of resampling techniques. It involves repeatedly sampling with replacement from a dataset, calculating the statistic of interest for each sample, and then using the resulting sample of statistics to estimate the sampling distribution. One of the […]

Boosting : Boosting is a machine learning ensemble method that combines multiple weak learners to create a stronger model. A weak learner is a model that performs slightly better than random guessing. The boosting algorithm works by training the weak learners sequentially, with each subsequent learner trying to correct the mistakes of the previous one. […]

BMI : Body mass index (BMI) is a measure of a person’s weight in relation to their height. It is commonly used to determine whether a person is underweight, normal weight, overweight, or obese. To calculate BMI, a person’s weight in kilograms is divided by their height in meters squared. For example, a person who […]

BLUE : A best linear unbiased estimator (BLUE) is a statistical estimator that satisfies the following three conditions: It is a linear function of the data. This means that the estimator can be expressed as a weighted sum of the observed values, where the weights are constants. For example, the sample mean is a linear […]

Blocking : Blocking is a technique used in data analysis to improve the efficiency and accuracy of statistical models by grouping similar observations together. This helps to reduce noise and improve the signal-to-noise ratio in the data, which in turn can improve the performance of the model. One common example of blocking is the use […]