Visual interpretation of slopes (or trend lines) is a source of poor interrater agreement (IRA) of graphed single-subject data. Difficulties with accurate or reliable slope interpretation may be overcome with newly discovered mathematic equations. Three experiments tested applications of the equations and demonstrated the following results: (1) manipulation of axis scaling (represented by a single numerical value called “GVQ”: Graphic Variability Quotient) strongly predicts the accuracy of behavior change (slope) ratings, β = .895, R2 = .801; (2) validation of a practical method for determining standard GVQ values was achieved (standardization is critical for reliable visual interpretation and comparison of data across graphs with uniquely constructed axes); and (3) a nonexpert group who received visual aids to rate slopes (degrees of a trend’s angle and a “slope change guide”) had significantly higher IRA (α = .956) than a control group that was using only predrawn trend lines, F(25, 27) = 3.11, p = .002, d = .49. The discussion explores how the results could become a basis for setting future standards in visual analysis that GVQ empirically and mathematically supports. Incorporating GVQ into graphic design and analysis can potentially improve IRA, improve measures of effect size that directly correspond with visual analysis, and facilitate between-study comparison of graphed single-subject data (in systematic reviews and meta-analyses).