Multicollinearity (Collinearity) is not a new term especially when dealing with multiple regression models. This phenomenon of relationship in between one response variable with the set of predictor variables also include models like classification and regression trees as well as neural networks. Collinearity is infamously famous for inflating the variance of at least one estimated regression coefficient, which can cause the model to predict erroneously and in a business setup it can have an…

Linear Model better known as linear regression is one of the most common and flexible analysis framework to identify relationship between two or more variables. The widely used linear model is represented by drawing the best fit line through a series of data points represented on a scatter plot. 

For any budding business analyst this should be the starting point to understand how model works at the very core of its design.

Selecting the Variables in Deducer…

As per the largest market research firm MarketsandMarkets the speech analytics industry will grow to USD 1.60 billion by 2020 at a Compound Annual Growth Rate (CAGR) of 22% from 2015 to 2020. Today the omnichannel world consists of voice, email, chat, social channels, and surveys, and each channel has its own importance.

Therefore, it becomes inevitable for any customer centric organization to ignore the information that can be glean…

As the world is getting more tech savvy and advancements made in the information technology especially in the healthcare industry has opened areas in data mining and machine learning. Within the area of data mining one technique which has gained a lot of popularity as well as skepticism among the auditors and fraud detectives is Benford’s Law or “The Law of First digit.

In the past some researchers in Canada used the Benford’s Law distribution to detect anomalies within the claims…

Best Subset Regression method can be used to create a best-fitting regression model. This technique of model building helps to identify which predictor (independent) variables should be included in a multiple regression model(MLR).

This method comprises of scrutinizing all of the models created from all possible permutation combination of predictor variables. This technique uses the R Squared value to check for the best model. Considering the level of complexity involved in creating…

As big data use cases proliferate in telecom, health care, government, Web 2.0, retail etc there is a need to create a library of big data workload patterns. . We have created a big data workload design pattern to help map out common solution constructs. There are 11 distinct workloads showcased which have common patterns across many business use cases.

1. Synchronous streaming real time event sense and respond workload
2. Ingestion of High velocity…