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.
Cost plus residual value is a method used to evaluate the worth of an asset based on both its original cost and its expected residual value at the end of its useful life. The residual value is the estimated value an asset will have at the end of its useful life, which is then added to the original cost to determine the total value or worth of the asset.
yes
-1.5
1
Answer: 1.5
ItS 2