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. 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). 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.
This type of analysis can help you predict the likelihood of an event happening or a choice being made. For example, you may want to know the likelihood of a visitor choosing an offer made on your website — or not (dependent variable). 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). Logistic regression models help you determine a probability of what type of visitors are likely to accept the offer — or not. As a result, you can make better decisions about promoting your offer or make decisions about the offer itself.
Predictive models built using this approach can make a positive difference in your business or organization. Because these models help you understand relationships and predict outcomes, you can act to improve decision-making. For example, a manufacturer’s analytics team can use logistic regression analysis as part of a statistics software package to discover a probability between part failures in machines and the length of time those parts are held in inventory. With the information it receives from this analysis, the team can decide to adjust delivery schedules or installation times to eliminate future failures.
In medicine, this analytics approach can be used to predict the likelihood of disease or illness for a given population, which means that preventative care can be put in place. Businesses can use this approach to uncover patterns that lead to higher employee retention or create more profitable products by analyzing buyer behavior. In the business world, this type of analysis is applied by data scientists whose goal is clear: to analyze and interpret complex digital data.
- Assess credit risk.
- Profile the consumers of packaged goods.
- increase profits in the banking industry.
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