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Logistic Regression Logistic Regression

Logistic Regression

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Table of Contents

Logistic regression is one of the types of Regression Analysis. 

Regression analysis is a statistical approach that is used to determine if there is any relationship between a dependent variable and the independent variable(s). It is a type of predictive model that helps forecast the outcome of the dependent variable with the use of two or more independent variables. 

Regression analysis includes two types of regression: Linear and Logistics Regression.

What is Logistic Regression?

Also known as Logit Model, the logistic regression model is used for predictive analytics and modeling. In statistics, it is used to predict the binary outcome of a categorical dependent variable using a set of independent variables. 

A binary outcome means that there are only two possible scenarios – 1 or 0. In statistical analysis, the dependent variable can assume two values – either binary regression (A or B) or multinomial regression (range of finite options). It is used to find out the relationship between dependent variables and a set of independent variables. 

Logistic regression is a better model to use when you are dealing with binary data. Binary data means that your dependent variable is dichotomous in nature. It falls into the categories such as yes/no, pass/fail, and so on. 

For example;

Logistic Regression Logistic Regression

You can use logistic regression as a predictive model to determine the likelihood of your customer base accepting a new promotional offer on your company’s app; or not. The options, of whether they accept or not, is your dependent variable. You may analyze customer behavior, history, or attitude on your app (independent variable). 

Logistic regression can help you predict what types of customers are most likely to accept the new promotional offer or not. This can assist you in making strategic decisions about your offers and promotions. 

Difference between Linear Regression and Logistic Regression:

Logistic Regression Logistic Regression

The difference between Linear Regression and Logistic Regression falls on the characteristic of the dependent variable. 

Linear regression is used in analysis when the dependent variable is continuous such as temperatures, rainfall, etc. 

Logistic regression is used when the dependent variable is categorical in nature – either binary (A or B) or multinomial (A, B, C, or D). 

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Assumptions for Logistic Regression

Dependent variable is binary: 

The dependent variable should be classified into two categories. This means logistic regression will predict the probability of an event into two scenarios – the event happens, 1, or the event does not happen, 0. 

Gaussian distribution: 

Logistic regression assumes that the relation between the variables (input and output) is linear. 

Independent variables should not have multi-collinearity:

This means that there should be no or very little correlation between the independent/ predictor variable. 

Larger sample size:

Logistic regression analysis requires a large sample size. A large sample size generates reliable results in analysis. 

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What are the types of Logistic Regression?

Logistic Regression Logistic Regression

There are three types of Logistic Regression: Binary, Multinomial, and Ordinal. 

Binary Logistic Regression: 

As a statistical approach, it is used to predict the relationship between two variables – the dependent variable, Y, and the independent variable, X. 

In this case, the dependent variable is binary (1 or 0) in nature, thus the name Binary Logistic Regression. This means the output can be yes/no, pass/fail, true/false, and so on. 

Multinomial Logistic Regression: 

In a Multinomial Logistic Regression model, you have one categorical dependent variable and two or more unordered outcomes. There is a probability of two outcomes. 

Ordinal Logistic Regression: 

Ordinal logistic regression implies that the dependent variable has a meaningful order. The variable may be classified into two or more categories such as agree/neutral/disagree or poor/good/average. 

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Importance of Logistic Regression

As mentioned before logistic regression is a predictive model. Using this model, businesses can make strategic decisions and make a positive difference. Logistic regression can help you understand relationships, estimate the probability and predict outcomes, thus helping you to make informed decisions. 

In marketing, you can use the model to predict if a targeted group of customers will buy a new product or not. 

For example, a beauty cosmetic company may want to understand if customers will positively respond to their promotional offer “Buy 2 Get Sample of New Skincare Range”. The company may use logistic regression to predict whether customers will “Respond” or “Not Respond” to the offer. The outcome will help them develop better marketing promotion. 

A company can predict the likelihood of employee turnover. This means the company can uncover the factors responsible for turnover. Thus they can make strategic changes in the workforce to boost employee retention. 

In medical science, logistic regression can be used to predict the likelihood of developing a disease, e.g., diabetes. The medical professional can make observations based on the characteristics of the patient, such as family medical history, blood tests, age, sex, and more. 

Logistic Regression helps predict the likelihood in two scenarios – yes or no. By predicting definite outcomes, it helps researchers make informed decisions based on statistical data.

Advantages of using Logistic Regression

Logistic regression extends application in Machine Learning: 

In Machine Learning, logistic regression is a much easier method to implement. You can describe a machine learning model as a mathematical representation of the real-world process. 

Machine learning applies statistical concepts to learning without any programming. So, when a machine is learning using binary classification, logistic regression is the best approach. 

Best approach for data set which is linearly separable:

Logistic regression is used when the Y variable, i.e., dependent variable, assumes only two values, A or B. 

Useful insight to make informed decisions: 

Logistic regression by nature provides information on the presence (or absence) of the relationship between the variables and also the direction of the relationship. 

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FAQs

A Logistic Regression is a type of Regression Analysis that is used to predict the binary outcome of an event. The dependent variable can assume either two values (binary) or a range of finite values (multinomial).

Logistic regression is a statistical approach used in business analytics to predict the likelihood of an event/ scenario. For example, a company can use it to predict whether the customers will visit/not visit, buy/ not buy, and so on.

Logistic regression is used when the dependent variable is categorical in nature – either binary (A or B) or multinomial (A, B, C, or D). 

Linear regression is used in analysis when the dependent variable is continuous such as temperatures, rainfall, etc.

The basic assumptions of Logistic Regression are: 

  • Dependent variable is binary
  • Gaussian distribution
  • Independent variables should not have multi-collinearity
  • Larger sample size

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