Is there any way or formula to find SSE with using SSR and SST? because i only have data for my ssr and sst and I need to know my sse or mse?
Yes, there is a way to calculate SSE (Sum of Squares Error) if you have the values for SSR (Sum of Squares Regression) and SST (Total Sum of Squares).
The formula to calculate SSE is:
SSE = SST - SSR
where SST represents the total variation in the data and SSR represents the variation explained by the regression model. Subtracting SSR from SST will give you the unexplained or residual variation, which is the SSE.
To calculate SSE, you need to know the values of SSR and SST. SSR is typically obtained from the regression output, while SST can be calculated as the sum of squares of the residuals. Here's how you can calculate SST:
1. Fit your regression model to the data.
2. Calculate the predicted values of the dependent variable using the regression model.
3. Calculate the residuals by subtracting the predicted values from the actual observed values.
4. Square each residual value.
5. Sum up all the squared residuals to get the value of SST.
Once you have the values for SSR and SST, simply subtract SSR from SST to calculate SSE. This will give you the unexplained variation or the sum of squared errors.