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
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Understanding Regression – Part 1
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.
Conjoint Analysis: Finding Customer Preferences
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.
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SNA: Understanding the Social
Increasingly, companies are harvesting their data to understand relationships between customers. Customer’s word of mouth promotion or denunciation of a product or company can be a vital piece of knowledge for organizations. Companies that can identify key influencers within a network are capable of utilizing those influencers to promote the product and affect the communication of information in the shortest path possible, in stark contrast to simply broadcasting information over traditional media. Continue reading SNA: Understanding the Social
Misusing Prediction Models
Basic predictions are one of the most fundamental aspects of data science. It provides insight that enables functional units such as marketing, logistics and finance to take action on the data. One of the most basic tools in the prediction area are regression models. Regression models take a number of “predictor” variables, i.e. variables that will be used to predict some outcome, and one or more “response”, i.e. outcome, variables. While the technique is very powerful, using it improperly can lead to disastrous effects. Continue reading Misusing Prediction Models