Correlation Analysis is a well-known analytical technique used to test if there is a relation between two quantities, for example did a marketing campaign lead to an increase in sales or as the price of one stock rises and falls does the price of another stock also rise or fall in synch.
Determining if there is a correlation can be exploited:
a marketing campaign that isn’t impacting sales can be cancelled to save costs or if it is successful it can then be extended or
ensuring that a portfolio has good diversity so that if one investment suddenly drops it is less likely that all other investments will also drop.
The aim of this article is to review what correlation analysis is and then use a worked example to understand how to implement correlation analysis using SAP Business Objects Web Intelligence.
The previous article looked at what moving averages are and how to calculate them. This article now looks at how to implement these in Web Intelligence.
The formula used here are compatible with the XIr3 version of SAP BOE however some formula may work in previous versions if available. We’ll begin by looking at how to calculate a simple moving average before looking at weighted and exponential forms.
This article looks at how to create a histogram in Web Intelligence and makes use of input controls to allow the user to automatically adjust the histogram’s bucket size.
Histograms is a charting technique used to analyse the distribution of a set of data for example a business analyst may use a histogram to analyse the range of ages of the employees of a business. Or in manufacturing you can use a histogram to analyse how stable a process is.
This article looks at different techniques to analyse joiners and leavers using Web Intelligence. Analysing joiners and leavers is where you need to understand the change in membership of a set, for example, products moving in and out of your top 10 sales. If you are able to identify which key products that are dropping in sales you can then adjust your marketing strategy to reverse the trend.
Other scenarios include
Customer analytics – customers can be categorise in terms of gold, silver bronze to indicate whether the customer generates high, medium or low revenue. We can then use joining and leaving analysis to identify which customers are moving between these groups
HR analytics – analysis of employee turnover or once identifying joiners then analysing this group for diversity metrics, for example, is a campaign to recruit more female members of staff working?
Healthcare – analysis of people by age group taking or dropping private healthcare
This article looks at how to do linear regression analysis in Web Intelligence. Linear regression is a statistical technique for analysing data in order to obtain a measure of correlation between two variables where the relationship between the variables is expected to be linear.
Web Intelligence does not contain a full set of formula for calculating the typical linear regression coefficients and so we must use other formula to calculate these. This article looks at how to apply the Least Squares method for calculating linear regression in web Intelligence.