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Logistic Regression

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Logistic Regression

Logistic Regression is a statistical classification technique that can be used in market research. It is also known by several other names including logit regression, or logit modelling.

Logistic Regression is similar to regular linear regression, as it is used to gain insight into the relationship between a dependent variable and either one or multiple independent variables. However, there are notable differences. Linear regression uses regular least-squares (as it is also known as least-square regression) to plot the line of best fit, which can then be used to predict the value of the dependent variable based on the knowledge of the independent variable.

On the other hand, logistic regression actually produces an estimate of the probability of a certain event occurring. This is a distinct difference, as logistic regression uses the knowledge of an independent variable to only predict whether it is probable that an event denoted by the dependent variable will either happen, or not happen, in a binomial fashion, rather than to predict the actual value of this dependent variable. As a result, the y value will be either 0 or 1, rather than being distributed along a line of best fit as is the case during linear regression.

The concept of odds ratios are crucial to logistic regression due to the very fact that logistic regression is based on the probability of the occurrence of an event. The dependent variable in logistic regression is actually what is known as a ‘logit’ (essentially a log of odds). This is the reason for the alternative name of logit regression for this statistical technique. A potential limitation to using logistic regression relates to the sample size. There must be a certain number of participants in the research for each set of dependent variables.

Examples of the use of logistic regression within Market Research could be to predict whether it is probable that a consumer would buy a product, given that their age was known.

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