As we head into the heat of the 2016 Presidential Campaign, the rhetoric will surely increase, but the outcome may ride on the backs of the mathematicians, statisticians, data scientists and computer scientists who will work to collect, churn and convey information to their respective campaigns in order to give their candidate an edge. A number of companies that specialize in analytics will be working tirelessly with data to find voters that can carry the day. Continue reading Big Data Could Sway The Presidential Election
The last article focused on what regression was and how the results can be interpreted. It mentioned that there were a number of assumptions required in order for the model to be valid. The assumptions are necessary because they relate to the reasons why a regression line works well as a prediction. The assumptions are based on the residuals, which are the difference between the predicted value of the dependent variable in the regression and the actual y value in the regression. Continue reading Understanding Regression – Part 2
Decision makers are always looking for ways to understand the effects of their actions. Managers generally assume that if they find a correlation between two items it means they understand the relationship between two variables; however, as was stated in a previous blog article, Beware of Correlations, correlations may not tell the whole story, and, furthermore, they can only tell the story between two variables. Regression allows us to understand more involved relationships between variables and an outcome variable.
Marketers are always looking for ways to identify which attributes of products their customers really like. Customers tend to choose products, but subconsciously they rate their preferences based on the attributes of those products. These attributes, also called stimuli, affect the customers decision making behavior.Conjoint analysis is a technique that allows the researcher to ascertain the value customers place on different attributes of a product or service, without specifically asking about the attributes themselves.
Its easy for large companies to spend large budget dollars on multi million dollar data initiatives, thereby taking risks that could yield significant rewards. Small to medium size companies dont always have the luxury of a large budget to build their analytics group or solution, and in many cases dont have the time, or patience, to wait for a solution to yield results. Large companies, while having the luxury of a budget, suffer from inertia, sometimes preventing progress.