Using a mixed methods approach, we explore a relationship between students' graph reasoning and graph selection via a fully online assessment. Our population includes 673 students enrolled in college algebra, an introductory undergraduate mathematics course, across four U.S. postsecondary institutions. The assessment is accessible on computers, tablets, and mobile phones. There are six items; for each, students are to view a video animation of a dynamic situation (e.g., a toy car moving along a square track), declare their understanding of the situation, select a Cartesian graph to represent a relationship between given attributes in the situation, and enter text to explain their graph choice. To theorize students' graph reasoning, we draw on Thompson's theory of quantitative reasoning, which explains students' conceptions of attributes as being possible to measure. To code students' written responses, we appeal to Johnson and colleagues' graph reasoning framework, which distinguishes students' quantitative reasoning about one or more attributes capable of varying (Covariation, Variation) from students' reasoning about observable elements in a situation (Motion, Iconic). Quantitizing those qualitative codes, we examine connections between the latent variables of students' graph reasoning and graph selection. Using structural equation modeling, we report a significant finding: Students' graph reasoning explains 40% of the variance in their graph selection (standardized regression weight is 0.64, p < 0.001). Furthermore, our results demonstrate that students' quantitative forms of graph reasoning (i.e., variational and covariational reasoning) influence the accuracy of their graph selection.