“Tea is an act complete in its simplicity.
When I drink tea, there is only me and the tea.
The rest of the world dissolves” – Thich Nhat Hanh
A picture is worth a thousand words, and numbers have the capacity to summarize a picture with just a few statistics, especially in today’s data driven world. The right perspective is necessary for the right kind of analysis. It is not just employing the right technique , but rather, it’s implementation determines the efficacy of the analysis and the relevance of the insight. Continue reading There’s No Free Lunch, Stupid
Visualizations are a great data exploration technique. Our human minds are better able to understand and retain visuals than scripts or text. Visualizations, apart from giving us a good general overview of the data, entail us with an intuitive understanding of the distribution of the dataset and its trends.
Continue reading Dispelling illusions using Visualizations
In what could possibly be a major impact to the analytics software industry, Microsoft recently announced a series of products, renaming the Revolution Analytics products, Microsoft R Server. The branding changes the Revolution Analytics brand products to Microsoft R Server and includes the following: Continue reading Microsoft Announces R Open and Makes R-Server Free Through Dreamspark
The R squared value, called the coefficient of determination, determines how well the data points fit on a regression equation. More specifically, the R squared value is a measure of how the independent variables in a regression equation explain the variables of the dependent variable. The value of R squared can change based on the inclusion or removal of variables in the regression model. R squared values are typically used as a measure of the effectiveness of a model. Hence, a high R squared value (anything above 55%), can be an indicator of a capable model. Continue reading The implication of R-Squared
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