Chapter 15 Technical Refreshers

Applied work often requires a timely return to foundational ideas rather than a complete review of every prerequisite course. This chapter provides project-focused refreshers in statistics, R and RStudio, Excel, and Power BI, with emphasis on choosing the right tool, checking calculations, preserving reproducibility, and producing outputs that others can inspect.

Learning outcomes

After completing this chapter, you should be able to:

• identify prerequisite concepts that require review for a project;
• use statistical summaries and inference without overstating conclusions;
• organize reproducible work in R and RStudio;
• build auditable calculations and summaries in Excel;
• create validated measures and relationships in Power BI; and
• select a tool according to the task, audience, and need for reproducibility.

Key terms

• Descriptive statistic: A numerical summary of observed data, such as a count, mean, median, proportion, or standard deviation.

• Statistical inference: The use of sample evidence and a probability model to draw qualified conclusions about a broader population or process.

• Practical significance: The degree to which a result is large or consequential enough to matter for a real decision.

• Reproducible workflow: An organized process in which data inputs, transformations, analysis, and outputs can be recreated and reviewed.

• Relative reference: A spreadsheet cell reference that changes when a formula is copied.

• Absolute reference: A spreadsheet cell reference that remains fixed when a formula is copied, usually marked with dollar signs.

• Data model: A structured set of tables, keys, relationships, and measures used for analysis.

• Measure: In Power BI, a calculation evaluated according to the current filter context.