Linear and Logistic Regression are popular techniques in the field of statistics and machine learning used for different types of tasks. Linear regression is used to study the relationship between two continuous variables and assumes that assumes that a linear relationship between the independent variable(s) and the dependent variable. It also assumes that the differences between observed and predicted values are independent of each other. The primary goal of this measure is to find the best-fitting straight line that describes the relationship between the independent variable(s) and the dependent variable. It is represented by the equation y=mx+b where $y$ represents the dependent variable, ???? represents the independent variable, ???? represents the slope of the line, and ???? represents the y-intercept. It helps to predict measures such as prices of items such as houses based on size, number of bedrooms.

Logistic regression is the measure used when the dependent variable is categorical. It is used for predicting categorical outcomes. Logistic regression predicts the probability that a given instance belongs to a particular category so that the ouput is a probability score between 0 and 1. Logistic regression utilizes logistic function of sigmoid function to model the probability. It is represented by the equation P(Y=1∣X)=1+e−(β0​+β1​X)1​ where P(Y=1∣X) represents the probability of the dependent variable being 1 given the independent variable ????, e represents the base of the natural logarithm, and β0​ and ????1 represents the coefficients of the model. This measure is commonly used for binary classification problems, including to predict whether an email is spam or not spam. It requires a larger sample size compared to linear regression, significantly affected by outliers, and that there is no perfect separation of the binary outcome variable by the predictor variables. APA