What is regression explain linear regression with an example?

Linear regression is commonly used for predictive analysis and modeling . For example, it can be used to quantify the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable).

What is regression explain with example?

Formulating a regression analysis helps you predict the effects of the independent variable on the dependent one . Example: we can say that age and height can be described using a linear regression model. Since a person's height increases as its age increases, they have a linear relationship.

What is regression in linear regression?

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data . One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.

What is linear regression explain in detail?

In the most simple words, Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable i.e it finds the linear relationship between the dependent and independent variabl

Why is it called regression?

"Regression" comes from "regress" which in turn comes from latin "regressus" - to go back (to something) . In that sense, regression is the technique that allows "to go back" from messy, hard to interpret data, to a clearer and more meaningful mode

What is regression and types of regression?

Regression is a method to determine the statistical relationship between a dependent variable and one or more independent variables . The change independent variable is associated with the change in the independent variables. This can be broadly classified into two major types. Linear Regression. Logistic Regression.

What does in regression mean?

1 : the act or an instance of regressing . 2 : a trend or shift toward a lower or less perfect state: such as. a : progressive decline of a manifestation of disease.