Without doubt, critical thinking is necessary in order to be a good analyst but particular skills and experience are also required. What are some of these skills?
Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.
Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?
It's critical to understand that statistical models are simplified representations of reality and they're all wrong but some of them are useful. So why do we use statistical models?
Neuroscience is very complex and advanced study of brain and people often misuse this term. Here we try to explain neuroscience terminologies and use of data science for such studies.
Despite the popularity of Regression, it is also misunderstood. Why? The answer might surprise you: There is no such thing as Regression. Rather, there are a large number of statistical methods that are called Regression, all of which are based on a shared statistical foundation.
Time series analysis is a complex subject but, in short, when we use our usual cross-sectional techniques such as regression on time series data, variables can appear "more significant" than they really are and we are not taking advantage of the information the serial correlation in the data provides.
This interview between Professor Andrew Gelman of Columbia University and marketing scientist Kevin Gray covers the basics of Bayesian statistics and how it differs from the ordinary statistics most of us learned in college.
Kevin Gray is president of Cannon Gray, a marketing science and analytics consultancy.
What is Structural Equation Modeling?
Time Series Analysis: A Primer
Bayesian Basics, Explained