Apple geek, analytics and AI fanatic, notorious project manager getting on your nerve with a smile.
In a similar vein as the previous post that dealt with analysis of variance (ANOVA) let’s shift our focus on a another problem solving approach, that is, linear regression. In principle, this model gives special prominence on the ability to predict an outcome based on a given set of variables.
Which trends prevail for 2017 and beyond within the realm of Enterprise Performance Management and what topics are most intensely discussed?
Find below a quick review of those topics that include:
Analytics adviser: the new role for the CFO
The 3 steps of the Prediction Analytics Cycle
Relevant use cases for the financial services industry
As in previous posts this paper is available for download either as pdf (ciber fin insight vol02)or you may generate it online via the LaTex service.
Analysis of variance (ANOVA) represents a common means to perform statistical tests as to whether there is a statistically significant difference among sample means. Though manual calculations are ok if you strive to understand the concept, however, larger examples quickly get tedious. Here, the long-standing open source R statistics package comes to our rescue. To show how R works for a simple ANOVA analysis the ensuing steps provide a quick intro as well a the source code for a fully working example.