Regression analysis is a statistical method used to examine the relationship between two or more variables. It is used to predict the value of one variable based on the values of other variables.
The two main types of regression are linear regression and logistic regression. Linear regression is used when the dependent variable is continuous, while logistic regression is used when the dependent variable is binary (e.g. yes or no).
Regression analysis is performed by fitting a line or curve to a set of data points that best represents the relationship between the variables. This line or curve is called the regression line or regression curve. The process involves choosing the best fitting line or curve based on a specific mathematical formula and using statistical measures to evaluate the accuracy of the model.
The purpose of regression analysis is to understand and quantify the relationship between variables. It can be used to predict future values, identify important variables, and identify trends or patterns in the data.
Regression analysis is commonly used in many fields, including social sciences, business, finance, and healthcare. It is used to predict stock prices, analyze market trends, determine the effect of advertising on sales, and identify risk factors for diseases. It is also used in research studies to examine the relationship between variables and to make predictions based on collected data.