This project aims to conduct a comprehensive linguistic analysis of a Ph.D. dissertation using advanced text mining and natural language processing techniques. By employing a diverse set of analytical methods, including word frequency analysis, sentiment analysis, topic modeling, named entity recognition, and readability assessment, among others, the study seeks to uncover deeper insights into the dissertation’s content, structure, and stylistic features. This multi-faceted approach will not only provide a quantitative understanding of the text but also offer qualitative insights into the dissertation’s themes, coherence, and overall academic contribution. The project’s findings are expected to demonstrate the potential of computational linguistics in enhancing the evaluation and understanding of complex academic texts, potentially paving the way for more sophisticated tools in academic writing and assessment.

The main aim of the study was to identify the main factors to consider while determining the price of cars in the US automotive market. The regression analysis revealed that the main factors that predicts the price of the cars in the automotive market include city mpg, horsepower, fuel system, engine size, cylinder number, fuel type, and wheel drive. In particular, this study revealed that besides city mpg, horsepower, and engine size, cars with mpfi fuel system, five and twelve cylinders, rear-wheel drive, and gas fuel type are expected to yield more profit in the US automotive industry. In agreement with this study finding, Singh and Mani (2016) argued that MPG is a major factor to be considered before buying a car. In fact, Singh, and Mani (2016) argued that cars with high city mpg are less cheaper than those with less city mpg because high city mpg implies more used engines due to the rough roads, frequent stopping due to traffic. The coefficient for horsepower was positive indicating that when the horsepower increases, the car prices increases. This was consistent with Ou et al. (2020), who found a positive relationship between horsepower, engine size, fuel type and number of cylinders.

ROS data

Calculating correlation coefficients for four continuous variables.

Two mechanics are changing oil filters for the arriving customers. The service time has an Exponential distribution with mean 12 minutes for the first mechanic, and mean 3 minutes for the second mechanic. When you arrive to have your oil filter changed, your probability of being served by the faster mechanic is 0.8.