The ability to understand the stochastic process that governs the changes in natural gas prices is crucial for many reasons. This paper aims to introduce the important methods widely used in econometrics, by linking them to a common 'use case' in the subject of commodity pricing. Using the data of natural gas' weekly prices from 2007 to 2014 of the German gas hub, the methods of least squares, maximum likelihood, machine learning gradient descent, and least squares optimisation are used to compute the coefficients of a multivariate causal regression analysis. This study also tests the short-term prediction of wholesale natural gas prices for each method used. It is found that where the linear approximation is not valid, the method suffers accordingly. However, the mathematical methods of gradient descent and least squares optimisation help visualise the data sets, highlight, and accentuate the nonlinear effects of several variables on the spot gas prices.