In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman’s rank correlation coefficient, multiple regression, Kruskal–Wallis, and Bonferroni correction statistical techniques via SPSS software to analyze the student data for academic years 2012, 2013, and 2014 to test the hypotheses. Only LA, PPK, and final exam (FE) scores were considered for this analysis. Research suggests that PPK influences student LA and FE performance. Similar analysis was conducted on the impact of LA on FE results regardless of students’ PPK levels. The results delivered mixed conclusions. Furthermore, the correlation coefficient results indicated that LA and FE were negatively correlated. However, the coefficient value was not sufficiently statistically significant to conclude that LA does not have an impact on FE results. On the other hand, the results of average LA on student FE results, with linear regression results, revealed that nonattendance of lectures had no effect on student performance in the FE. The multiple regression results of our study identified that, PPK in a regression model, is a good fit of the data, but LA in a regression model is not a good fit of the data.