It is possible to fit curves to a specific formula if you know something about it. For example if you have a resistor charging a capacitor from a DC voltage source the voltage rises in a specified way, an exponential curve, so you could use this known formula and find the coefficients.
There is often an iterative process (repeated, in a software loop perhaps) that can find coefficients by closing on a result if you can work out a formula. This uses a purpose built program. It just tries different values logically till the answer is found. Successive approximation. Works for those like me that are not mathematically inclined. Some expert might sniff at this, but whatever works.
There are also more general formulas.
The most basic is a linear regression for Y = slope + offset which fits a straight line to your X, Y data.
There are also polynomial regressions that fit a type of curve according to the order.
You can find an order blindly, but going to a higher order means the fitted curve will follow bumps and wriggles that are perhaps not reasonable. Basically plot the curve with points shown, and use your eyeball to estimate or count the bumps, and add one. Thus first order is constant, second order is a single curve (a hump or hollow), third order is S shaped, 4th order M or W shaped etc. These shapes can be sloped or inverted.
Excel can do polynomial and linear regressions, as well as match curves to a range of more specific formulas. This lets you calibrate a sensor for example with real values, then fit a curve so you can interpolate data points properly, for example when reading the sensor, convert the voltage readings to calibrated readings of sensor input. Often the sensor is a second or third order curve, and you can be reasonably sure outliers or sudden steps in the curve are not realistic, so it means a better calibration technique is needed. More data points gives greater confidence.
What to do with points that are outliers on your plot of the data? If you are sure that it is a measurement error, delete it. See the link on outliers below. This requires some understanding and judgement. You might be making an assumption the noise (outlier point) is a normal distribution when it isn't.
http://www.tushar-mehta.com/publish_train/data_analysis/16.htm
http://stackoverflow.com/questions/10857252/quadratic-and-cubic-regression-in-excel
http://en.wikipedia.org/wiki/Degree_of_a_polynomial
http://en.wikipedia.org/wiki/Outlier