# What does a high R-squared mean?

## What does a high R-squared mean?

The most common interpretation of r-squared is how well the regression model fits the observed data. For example, an r-squared of 60% reveals that 60% of the data fit the regression model. Generally, a higher r-squared indicates a better fit for the model.

## What does it mean to have a negative R-squared?

The negative R-squared value means that your prediction tends to be less accurate that the average value of the data set over time.

What does an r2 value of 0.5 mean?

Any R2 value less than 1.0 indicates that at least some variability in the data cannot be accounted for by the model (e.g., an R2 of 0.5 indicates that 50% of the variability in the outcome data cannot be explained by the model).

### What does an r2 value of 0.6 mean?

Hello Darshani, An R-squared of approximately 0.6 might be a tremendous amount of explained variation, or an unusually low amount of explained variation, depending upon the variables used as predictors (IVs) and the outcome variable (DV).

### What does an r2 value of 0.1 mean?

R-square value tells you how much variation is explained by your model. So 0.1 R-square means that your model explains 10% of variation within the data. The greater R-square the better the model.

What does an r2 value of 0.2 mean?

R-squared is a measure of how well a linear regression model “fits” a dataset. In the output of the regression results, you see that R2 = 0.2. This indicates that 20% of the variance in the number of flower shops can be explained by the population size.

#### What does an R2 value of 1 mean?

perfect fit
An R2=1 indicates perfect fit. That is, you’ve explained all of the variance that there is to explain. In ordinary least squares (OLS) regression (the most typical type), your coefficients are already optimized to maximize the degree of model fit (R2) for your variables and all linear transforms of your variables.