# There’s No Free Lunch, Stupid

“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

# Dispelling illusions using Visualizations

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

# The implication of R-Squared

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

# Understanding Regression – Part 2

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