Best practices in data acquisition and entry are the central theme of this chapter.Correct entry of variables and data in spreadsheets like Excel is discussed along with common problems of data entry that may prevent software from reading and analyzing data correctly. Typical practices of entering data for nominal, ordinal, and interval variables give the student information on how to enter data in Excel for these variables.The purpose of codebooks and composing them to match data are discussed.Different types of data including cross-sectional, time-series, and panel are presented to the student.Finally, common sources of public administration data are listed and described.

Chapter 2 is a deep dive into the threads that link the chemical and mineralogical makeup of soils to that of the surrounding cosmos. The first section examines elements across the periodic table, and how they systematically change in abundance due to a variety of cosmic processes. The second part of the chapter examines how the elements are combined into minerals, and especially the silicate minerals. Discussion of the factors that dictate mineral stability in soils is introduced, and these will be examined in even greater depth in Chapter 8. Secondary minerals are also introduced, again with a strong focus on the silicate group. Cation exchange is examined. The chapter ends with the effect of plants, and biology, on soil chemistry, which is expanded upon in Chapter 3. The activities at the conclusion of the chapter provide students with an opportunity to use spreadsheets and data analyses in order to gain experience and confidence in data analysis.

Tabular is a domain-specific language for expressing probabilistic models of relational data. Tabular has several features that set it apart from other probabilistic programming languages including: (1) programs and data are stored as spreadsheet tables; (2) programs consist of probabilistic annotations on the relational schema of the data; and (3) inference returns estimations of missing values and latent columns, as well as parameters. Our primary implementation is for Microsoft Excel and relies on Infer.NET for inference. Still, the language can be called independently of Excel and can target alternative inference engines.

Pencil, paper and a scientific calculator are often adequate when analysing small amounts of data. However, spreadsheets are favoured especially when large amounts of data are involved. This chapter explores some of the basic features of spreadsheets and show how they may be applied to the analysis and presentation of experimental data. The Excel spreadsheet program by Microsoft is highlighted due to its availability, power, and longevity. Specific features of Excel are discussed such as the LINEST function for fitting equations, and the Analysis ToolPak which contains a number of useful analysis tools.