Numerical Python : A Practical Techniques Approach for Industry

© Robert Johansson 2015

Robert JohanssonNumerical Pythonhttps://doi.org/10.1007/978-1-4842-0553-2_14

14. Statistical Modeling

Robert Johansson^1 

(1)

Chiba, Chiba, Japan

 

Keywords

Design MatrixResult ObjectData FrameGeneralize Little SquareDesign Matrice

In the previous chapter we covered basic statistical concepts and methods. In this chapter we build on the foundation laid out in the previous chapter and explore statistical modeling, which deals with creating models that attempt to explain data. A model can have one or several parameters, and we can use a fitting procedure to find the values of the parameter that best explains the observed data. Once a model has been fitted to data, it can be used to predict the values of new observations, given ...