Residual point
A residual is defined in the context of some "expected" value. There is no information in the question regarding expected values.
The point lies one unit above the regression line.
The point lies 1 unit below the regression line.
Ah, the stochastic error term and the residual are like happy little clouds in our painting. The stochastic error term represents the random variability in our data that we can't explain, while the residual is the difference between the observed value and the predicted value by our model. Both are important in understanding and improving our models, just like adding details to our beautiful landscape.
The residual for a particular point in a regression is negative if the estimated or fitted value at that point is greater than the observed value.
Residual point
If a data point has a residual of zero, it means that the observed value of the data point matches the value predicted by the regression model. In other words, there is no difference between the actual value and the predicted value for that data point.
A residual is defined in the context of some "expected" value. There is no information in the question regarding expected values.
The point lies one unit above the regression line.
Residual value is the future value of a good after depreciation of its initial value. For example you bought a car for $20,000. After two years and 60,000 of mileage it will value of $10,000.
The residual value of "cost plus" is whatever is charged which exceeds the cost. Example: I provide a quote the terms for a project as being "cost plus 20%". If the cost for my project is $100, then I would bill $120. The residual value is $20.
yes
residual value
Residual value
ItS 2
1