Interpolation is an estimation of a value within two known values in a sequence of values. Polynomial interpolation is a method of estimating values between known data points. When graphical data contains a gap, but data is available on either side of the gap or at a few specific points within the gap, interpolation allows us to estimate the values within the gap.
We could use our function to predict the value of the dependent variable for an independent variable that is in the midst of our data. In this case, we are performing interpolation.
Suppose that data with x between 0 and 10 is used to produce a regression line y = 2x + 5. We can use this line of best fit to estimate the y value corresponding to x = 6. Simply plug this value into our equation and we see that y = 2(6) + 5 =17. Because our x value is among the range of values used to make the line of best fit, this is an example of interpolation.
Extrapolation is an estimation of a value based on extending a known sequence of values or facts beyond the area that is certainly known. In a general sense, to extrapolate is to infer something that is not explicitly stated from existing information.
When we predict values that fall within the range of data points taken it is called interpolation. When we predict values for points outside the range of data taken it is called extrapolation. Extrapolation over too far a range can be dangerous unless it is certain that the relationship between the variables continues over the entire range.
- There are no sudden jumps in the values of dependent variable(Y) from one period to another(X).
- The rate of change of figures (Y) from one period to another(X) is uniform.
- There will be no consecutive missing values in the series.