Is linear regression too simple?

As far as your type of analysis – I agree that a simple linear regression could be considered too simplistic. If you want to use regression analysis, perhaps a standard linear multiple regression, or hierarchical regression, might be considered.

Why is it called simple linear regression?

Simple linear regression gets its adjective “simple,” because it concerns the study of only one predictor variable. In contrast, multiple linear regression, which we study later in this course, gets its adjective “multiple,” because it concerns the study of two or more predictor variables.

What is the difference between linear and non linear regression?

Nonlinear regression is a form of regression analysis in which data is fit to a model and then expressed as a mathematical function. Simple linear regression relates two variables (X and Y) with a straight line (y = mx + b), while nonlinear regression relates the two variables in a nonlinear (curved) relationship.

What is a linear regression for dummies?

Linear regression attempts to model the relationship between two variables by fitting a linear equation (= a straight line) to the observed data. One variable is considered to be an explanatory variable (e.g. your income), and the other is considered to be a dependent variable (e.g. your expenses).

How do you use ya bX?

You might also recognize the equation as the slope formula. The equation has the form Y= a + bX, where Y is the dependent variable (that’s the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept.

What is linear regression for dummies?

Linear regression attempts to model the relationship between two variables by fitting a linear equation (= a straight line) to the observed data. If you have a hunch that the data follows a straight line trend, linear regression can give you quick and reasonably accurate results.