Linear and Logistic Regression Using RStudio

Linear and logistic regression are popular statistical models used in data analysis and prediction. In RStudio, we can perform linear and logistic regression using built-in functions and packages.

Linear regression is a statistical method to establish the relationship between a dependent variable and one or more independent variables. The objective of linear regression is to find a linear equation that best fits the data. In RStudio, we can perform linear regression using the lm() function. The lm() function takes two arguments, the formula specifying the relationship between the dependent and independent variables and the dataset containing the variables. Linear regression and logistic regression are two popular statistical models used in data analysis and prediction. In RStudio, we can perform both linear and logistic regression using built-in functions and packages.

In logistic regression, we use a binary dependent variable to predict the probability of an event. The dependent variable in logistic regression is a categorical variable that can take only two values, typically 0 and 1. The independent variables can be either categorical or continuous. The logistic regression model predicts the probability of the dependent variable being one based on the values of the independent variables. In RStudio, we can perform logistic regression using the lm () function.