Regression Analysis
Regression analysis is a statistical method used in data analytics to examine the relationship between a dependent variable and one or more independent variables. The aim of regression analysis is to find the best-fitting line or curve that describes the relationship between the variables.
In regression analysis, the dependent variable is the variable that is being predicted or explained, while the independent variable(s) are the predictor(s) or explanatory variable(s). The relationship between the dependent variable and independent variable(s) is modeled using a mathematical equation, which can be used to make predictions about the dependent variable based on the values of the independent variable(s).
There are two main types of regression analysis: linear regression and nonlinear regression. Linear regression models the relationship between the dependent variable and independent variable(s) as a straight line, while nonlinear regression models the relationship as a curve or other nonlinear function.
Regression analysis can be used in various applications, such as predicting sales based on advertising expenditure, predicting house prices based on location and other variables, and predicting the likelihood of a patient developing a disease based on their age, gender, and other factors.
Regression analysis is a powerful tool in data analytics for understanding the relationship between variables and making predictions based on that relationship.
In regression analysis, the dependent variable is the variable that is being predicted or explained, while the independent variable(s) are the predictor(s) or explanatory variable(s). The relationship between the dependent variable and independent variable(s) is modeled using a mathematical equation, which can be used to make predictions about the dependent variable based on the values of the independent variable(s).
There are two main types of regression analysis: linear regression and nonlinear regression. Linear regression models the relationship between the dependent variable and independent variable(s) as a straight line, while nonlinear regression models the relationship as a curve or other nonlinear function.
Regression analysis can be used in various applications, such as predicting sales based on advertising expenditure, predicting house prices based on location and other variables, and predicting the likelihood of a patient developing a disease based on their age, gender, and other factors.
Regression analysis is a powerful tool in data analytics for understanding the relationship between variables and making predictions based on that relationship.
