News / Blog: #analysis

What is regression analysis?

Regression analysis is a mathematical technique used to predict future outcomes and determine the relationships between different variables. It can be used to predict the effect of a particular influencing factor on another variable or to determine which variables contribute most strongly to a particular metric. Applications of regression analysis include predicting sales volumes, determining prices, and determining credit risk.

What are the Gauss-Markov assumptions?

The Gauss-Markov assumptions are a group of assumptions used in linear regression analysis. They include the assumption that the influencing factors (the predictor variables) are independent of each other, the relationship between the influencing factors and the dependent variable is linear, the variance of the dependent variable is constant, and the residuals are normally distributed. The Gauss-Markov assumptions form the basis for linear regression analysis.

What is probability theory?

Probability is a branch of mathematics that deals with the analysis of random variables, probabilities and probability distributions. It is used to solve problems associated with probabilities and likelihoods that arise in many areas of science and everyday life. It is an important part of statistics, where probabilities are used to describe events and predict future events.

What is predictive analytics?

Predictive analytics is a method of data analysis that enables companies to predict how certain events are likely to develop. It uses various mathematical models to analyze existing data to make predictions. Predictive analytics can be used in many industries, for example, in the financial industry to predict the development of the stock market, or in the retail industry to predict the demand for certain products.

What is cluster analysis?

Cluster analysis is a descriptive statistics technique used to form similar groups of objects. It is a procedure in which data points that are similar with respect to certain characteristics are grouped together. The goal of cluster analysis is to create groups that are as homogeneous as possible. Cluster analysis can be used in various application areas such as market research, demographic analysis, medical research and credit.