As a result, you can make better decisions about promoting your offer or make decisions about the offer itself. Logistic regression models help you determine a probability of what type of visitors are likely to accept the offer - or not. Your analysis can look at known characteristics of visitors, such as sites they came from, repeat visits to your site, behavior on your site (independent variables). For example, you may want to know the likelihood of a visitor choosing an offer made on your website - or not (dependent variable). This type of analysis can help you predict the likelihood of an event happening or a choice being made. It is used in statistical software to understand the relationship between the dependent variable and one or more independent variables by estimating probabilities using a logistic regression equation. In this analytics approach, the dependent variable is finite or categorical: either A or B (binary regression) or a range of finite options A, B, C or D (multinomial regression). This type of statistical analysis (also known as logit model) is often used for predictive analytics and modeling, and extends to applications in machine learning.
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