Understanding Logistic Regression Analysis & Why you need it?

Whether you are a market researcher who needs to make accurate predictions about new product launches, or a wildlife biologist keen on studying the successful breeding of wolf packs in the Rocky Mountains, you can make reliable predictions, by using the powerful Logistic Regression Analysis (LRA) model.

Understanding Logistics Regression Analysis (LRA)

You may find, that it is often useful or sometimes even necessary to analyze and predict a discrete set of outcomes. For example, you may wish to predict the likely success/failure rate of a new product or the likelihood of customer retention/loss. In statistics, we use regression analysis to predict the result of a categorical dependent variable based on one or more predictors or independent variables. This is called Logistic Regression Analysis (LRA). The outcome of a logistic analysis is usually measured with a dichotomous variable that can assume only two discrete values.

In Logistic Regression, the connection between the categorical dependent variable and the continuous independent variables is measured by changing the dependent variable into probability scores. Unlike linear regression models, which are used to predict a continuous outcome variable, logistic regression models are mostly used to predict a dichotomous categorical outcome, LRAs are frequently used in business analysis applications.

An application may use logistic analysis to determine consumer behavior. For example, you can analyze if a customer will purchase a product or not. While binomial / binary logistic regression refers mostly to two possible outcomes usually coded as "0" and "1", multinomial logistic regression refers to three or more possible outcomes, such as yes/no/maybe scenarios for purchasing products.

The Applications and Benefits of using LRA

Consider using logistic analysis if you would like to predict discrete outcomes. For example, whether a voter will vote for a Democrat or Republican can be determined through the interpretation of logistic modeling, which is based on demographic parameters such as gender, age and the state of residence of the voter. Logistic regression has varied applications in marketing, healthcare and social sciences.

In business, LRA is suited to data mining applications which are used in business analytics. For example, logistic regression modeling can be used to predict customer retention, such as a yes/no/maybe scenario indicating, whether a customer will re-visit/not visit again/may visit again based on the marketing stimuli.

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