Validating a model using statistics

This activity includes supplemental materials, including background information about the topics covered, a description of how to use the application, and exploration questions for use with the java applet.Cluster: Summarize, represent, and interpret data on a single count or measurement variable.If the residual analysis does not indicate that the model assumptions are satisfied, it often suggests ways in which the model can be modified to obtain better results.In regression analysis, model building is the process of developing a probabilistic model that best describes the relationship between the dependent and independent variables.Because their goal is reliable inference, many of the methods of classical statistics revolve around identifying and eliminating sources of bias.So, for a classical statistician, model validation is much more involved than applying CV and selecting the model with the maximum accuracy/minimum error.However, I came across an article where it was mentioned that core statisticians do not treat these above methods as their go-to validation techniques.I have been wondering ever since about the validation techniques that hard-core statisticians consider and/or use as model validation techniques.

In building models it is often desirable to use qualitative as well as quantitative variables.The color, thickness, and scale of the graph are adjustable which may produce graphs that are misleading.Users may input their own data, or use or alter pre-made data sets.In grades 6 – 8, students describe center and spread in a data distribution.Here they choose a summary statistic appropriate to the characteristics of the data distribution, such as the shape of the distribution or the existence of extreme data points.

Leave a Reply