Correlations and linear regression analyses are useful tools for evaluating and expressing the nature of the relationships observed between the variables of interest. While correlations are observed associations between two variables, the goal of employing simple linear regressions is to create a predictive model between a continuous dependent variable and one independent variable. This paper used correlations and simple linear regressions to examine factors influencing reading and math scores among school children and to discuss the results obtained from the analyses conducted. In order to better interpret the results, various statistical tests (e.g., histograms, simple scatter plots) that are necessary to support the findings are performed. Essentially, the first chapter presents the multiple correlation (Pearson and Spearman) coefficients and their p-values computed using SPSS to investigate the relationship between social studies and math scores and reading scores considered simultaneously. It further discusses partial correlations and analyzes whether gender partly explains the relationship between reading and social studies by, for instance, suppressing the correlation between them. The second chapter involves the use of SPSS to examine the relationship between certain variables in the examined database by finding the best straight line through the data. Two simple linear regression models are performed and the results are interpreted properly. The research questions raised are "Do social studies reliably predict reading scores?" and "Do math scores reliably predict reading scores?" In both cases, the independent variable reliably predicted the dependent variable. Still, such analyses provide hints about these relationships, and experimental studies are required to examine them in details and test the hypotheses derived from this preliminary study. Finally, the third chapter draws conclusions based on the findings.