The phrase "approximation to the data" is correct and usable in written English.
It can be used in contexts where you are discussing how closely a model or calculation reflects the actual data.
Example: "The approximation to the data was satisfactory, allowing us to make informed predictions."
Alternatives: "fit to the data" or "estimate of the data".
Asymptotic results, that allow the number of variables to grow faster than the number of observations, demonstrate that the worst possible expected error rate of the proposed rules converges to the error of the optimal Bayes rule when the postulated model is true, and to a slightly larger constant when this model is a reasonable approximation to the data generating process.
The red curve represents the assembled strong learner model; which could be seen as a better approximation to the data.
However, if momentarily we treat λ as an adjustable parameter, we can improve the approximation to the data in Fig. 1 (solid lines in the top panel), which suggests that the concept of an interfacial layer indeed possesses significance.
Also include is a least-square fitted quadratic (at 2000 km linear) approximation to the data (black curve), new Te model (blue solid), the IRI Te model (IK option) (blue dashed), the FLIP model (red), and the ISR averages from the Millstone Hill model (green).
This curve would also be a reasonable approximation to the data from Yugoslavia, Sweden and Switzerland.
In general, the model with the lowest AIC is considered the best approximation to the data.
When compared to the other models, this specification reaches the highest level of the likelihood function, which would indicate a better approximation to the data-generating process.
Power of sampling schemes was derived by a simulation where nine transect designs of equal effort detected population declines over 10 years within an 80 km × 2.5 km sampling unit either with or without stratification, based on data-generating distributions that were an approximation to the observed data.
In fact, the POD technique offers an orthogonal basis to the given data, i.e., offers an optimal low order approximation to the given data.
The technique constructs a B-spline representation with guaranteed quality of approximation to the original data.
Without a fitting parameter, the diffusivities calculated in silicalite give a good approximation to the experimental data.
