Given the current defective parts that are being created within the Expanded Polypropylene (EPP) automated machines in automotive manufacturing, a focus was developed to study the EPP process and investigate if defect creation could be anticipated based on sensor readings. Statistical analysis methods are used to analyze data collected from the sensors at an EPP press at a local Guelph auto parts manufacturer. Data were collected from different sensors in the press and were analyzed against the output produced. Linear regression, Pearson correlation, logistic regression, and bi-serial correlation are the four statistical methods used for this study. This research investigates an automotive EPP process to understand how it operates in addition to testing some statistical methods to determine if they could be useful in defect prediction. This study confirmed that not all the statistical techniques used were useful for this application. Of the four methods considered, one, bi-serial correlation was found to give interesting results.