This article presents exploratory data analytic methodology for visualizing and summarizing data that can be represented as individual-specific curves. We propose a simplified form of functional data analysis. A nonparametric scatterplot smooth is applied to each individual's data, followed by a principal components analysis of the smoothed data. We then display the individual smooth curves in an array organized by principal component scores. The display suggests interpretable summary measures. The methodology is applied to the measurement of proliferative activity, a biomarker for colon cancer risk. We use the summary measures in the analysis of a pilot study clinical trial.