an introduction to statistical learning uic

We also have information regarding each employee’s education level and the year in which the wage was earned. However, it is also clear from Figure 1.1 that there is a significant amount of variability associated with this average value, and so age alone is unlikely to provide an accurate prediction of a particular man’s wage. Given an employee’s age, we can use this curve to predict his wage. On average, wage increases with the level of education. Right: Boxplots displaying wage as a function of education, with 1 indicating the lowest level (no high school diploma) and 5 the highest level (an advanced graduate degree). There is a slow but steady increase of approximately $10,000 in the average wage between 20. On average, wage increases with age until about 60 years of age, at which point it begins to decline. Wage data, which contains income survey information for males from the central Atlantic region of the United States. The blue line, which provides an estimate of the average wage for a given age, makes this trend clearer.įIGURE 1.1.

There is evidence that wage increases with age but then decreases again after approximately age 60. Consider, for example, the left-hand panel of Figure 1.1, which displays wage versus age for each of the individuals in the data set. In particular, we wish to understand the association between an employee’s age and education, as well as the calendar year, on his wage. In this application (which we refer to as the Wage data set throughout this book), we examine a number of factors that relate to wages for a group of males from the Atlantic region of the United States. To provide an illustration of some applications of statistical learning, we briefly discuss three real-world data sets that are considered in this book. With unsupervised statistical learning, there are inputs but no supervising output nevertheless we can learn relationships and structure from such data. Problems of this nature occur in fields as diverse as business, medicine, astrophysics, and public policy.

Broadly speaking, supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs. These tools can be classified as supervised or unsupervised. Statistical learning refers to a vast set of tools for understanding data.

An introduction to statistical learning uic