In this chapter, we provided a brief introduction to the basic statistical analysis techniques you may encounter in data science applications. We started with simple techniques to calculate the mean, median, and mode of a set of numerical data. Both standard Java and third-party Java APIs were used to show how these attributes are calculated. While these techniques are relatively simple, there are some issues that need to be considered when calculating them.

Next, we examined linear regression. This technique is more predictive in nature and attempts to calculate other values in the future or past based on a sample dataset. We examine both simple linear regression and multiple regression and used Apache Commons classes to perform the regression and JavaFX to draw graphs.

Simple linear regression uses a single independent variable to predict a dependent variable. Multiple regression uses more than one independent variable. Both of these techniques have statistical attributes used to assess how well they match the data.

We demonstrated the use of the Apache Commons OLSMultipleLinearRegression class to perform multiple regression using cigarette data. We were able to use multiple attributes to create an equation that predicts the carbon monoxide output.

With these statistical techniques behind us, we can now examine basic machine learning techniques in the next chapter. This will include a detailed discussion of multilayer perceptrons and various other neural networks.