Differentiate linear regression from logistic regression

Hey everyone! I just wanted to jump in and share my thoughts and knowledge on the topic of differentiating linear regression from logistic regression, based on this source.

Linear regression and logistic regression are both powerful statistical techniques, but they have distinct differences in terms of their applications and methodologies.

Linear regression is commonly used when the dependent variable is continuous, meaning it can take any numerical value. It aims to establish a linear relationship between the dependent variable and one or more independent variables. The equation y=mx+c, where y represents the dependent variable, m is the slope, x is the independent variable, and c is the intercept, is used in linear regression.

On the other hand, logistic regression is employed when the dependent variable is categorical in nature, with discrete outcomes such as binary (yes/no) or multinomial (multiple categories). The equation y=ex+e-x is used in logistic regression, where e represents the base of the natural logarithm.

Another distinction lies in the interpretation of coefficients. In linear regression, the coefficient interpretation is straightforward, as it represents the change in the dependent variable for a unit change in the independent variable. However, in logistic regression, the interpretation depends on the specific family and link functions chosen.

In terms of methodology, linear regression typically employs the ordinary least squares method to minimize the error between the predicted and actual values, aiming to find the best-fit line. In contrast, logistic regression employs the maximum likelihood method to estimate the parameters that maximize the likelihood of observing the given data.

I hope this clarifies the differences between linear regression and logistic regression for everyone. Feel free to share your own insights and experiences on this topic!