If there is a perfect linear relationship between the explanatory variable and the response variable there will be some variation in the values of the response variable because of the variation that exists in the values of the explanatory variable. In any real data there will be more variation in the values of the response variable than the variation that would be explained by a perfect linear relationship. The total variation in the values of the response variable can be regarded as being made up of variation explained by the linear regression model and unexplained variation.
The coefficient of determination is the proportion of the explained variation relative to the total variation. If the points are close to a straight line then the unexplained variation will be a small proportion of the total variation in the values of the response variable. This means that the closer the coefficient of determination is to 1 the stronger the linear relationship.
The coefficient of determination is also used in more advanced forms of regression, and is usually represented by R2. I've found another similar question here but the answer didn't help me understand it.
I'd really appreciate some help! I think what you are referring to is the definition of coefficient of determination R-squared.
It shows that how much variation in Y is explained by the variable X. For example if your r-squared is 0. Its used as a measure of goodness of fit of the model. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group.
Create a free Team What is Teams? Learn more. What do we mean by saying "Explained Variance" [duplicate] Ask Question. Asked 2 years, 6 months ago. Active 2 years, 6 months ago. Viewed 3k times. Improve this question. This is what's meant by explained variance.
0コメント