A list of numbers by itself rarely tells the whole story. To understand what data are telling us, we need ways to organize, visualize, and summarize them. Graphs can reveal patterns that are difficult to see in a table, while numerical summaries allow us to describe and compare datasets efficiently.In this module, you will focus on quantitative data and learn how to represent distributions using frequency tables, dotplots, histograms, and boxplots. You will examine the key features of a distribution, including its shape, center, spread, gaps, and potential outliers, and learn how measures such as the mean, median, standard deviation, and interquartile range capture different aspects of the data. You will also explore how statistics such as z-scores and the 95% rule can help put individual observations into context and allow us to compare values across different distributions. Along the way, you will consider an important principle of statistical analysis: the best summary depends on the characteristics of the data.Finally, you will use R and RStudio to create visualizations and calculate summary statistics. By the end of the module, you should be able to move from a dataset to a clear statistical description, and communicate what the data reveal in a meaningful and accurate way.

Statistics becomes especially powerful when we move beyond describing a single variable and begin examining relationships between variables. In this module, you will use scatterplots and correlation to identify patterns between quantitative variables and describe their direction, form, and strength. You will then build linear regression models, interpret slopes and intercepts in context, and use these models to make predictions. You will also evaluate how well a model fits the data using residuals and RMSE, while considering the important distinction between interpolation and extrapolation. Good statistical analysis also requires knowing when patterns can be misleading. You will examine how graphs, data sources, and additional variables can influence the conclusions we draw, including examples involving misleading visualizations and Simpson's Paradox. Finally, you will begin the transition from descriptive statistics to statistical inference by exploring random sampling, sampling distributions, and standard error. By the end of the module, you should be able to analyze relationships in data, assess the strengths and limitations of linear models, and communicate conclusions responsibly.

This final module brings together the major ideas and techniques developed throughout the course. You will review how data are collected, organized, visualized, and summarized, and revisit the tools used to describe distributions and relationships between quantitative variables. You will also practice applying linear modeling and regression techniques, giving you an opportunity to connect concepts from across Modules 1, 2, and 3 and prepare for the final assessment. The module concludes with the final exam, where you will demonstrate your understanding of the fundamental principles of data analysis. The exam will ask you to interpret statistical information, select and apply appropriate methods, and draw meaningful conclusions from data. Successful completion of the exam will demonstrate your ability to approach statistical questions thoughtfully, accurately, and independently.