S470 | 11818 | Karen Kafadar

How do you analyze data? When faced with data from various sources, of various types, what questions should one ask, and what clues can we find in the data to further our understanding? Statistics, broadly defined, is the science of and art of analyzing data. Many statistical procedures require formal probability model structures with parameters, and statistical methods offer tools for estimating those model parameters. Sometimes the assumptions governing those models hold, but often they do not. What analyses can provide insight into the data and the underlying mechanisms while being insensitive to model assumptions? Nonparametric methods are distribution-free, but some prior analysis is needed to understand the data. Exploratory data analysis is a philosophy of analyzing data. The ubiquity of data and the emergence of "data mining" makes this course essential for anyone who wants to analyze data. In this course, we will learn many different tools for data analysis as well as the commands and programs in R (free statistical software) for conducting these analyses. Some prior familiarity with statistical methods is assumed. Those who have had formal statistics courses can take the course at a higher level, where connections between EDA tools and mathematical statistical methods will be developed. This course is valuable to anyone who has data to analyze. It is also a lot of fun; students learn a lot. Course objectives: Introduce philosophy of exploratory data analysis; Teach tools for the analysis of data; Provide opportunities for analyzing data (R/S-Plus); Demonstrate the value of oral/written communication skills; Offer experience in preparing oral and written reports of data analyses. Topics: The philosophy of exploratory versus confirmatory data analysis Summarizing batches of data: Stem-and-leaf diagrams, boxplots, qq plots, Data Transformations (ladder of re-expressions), Jackknife and bootstrap, Two-way and three-way analyses (median polish), Standardization, Fitting robust-resistant lines (least absolute deviations), Analyzing count data