The research project titled "Investigation of the Factors That Influence the Price of Cars in the USA" aimed to identify key features that can help a new automotive manufacturing company gain a competitive edge in the highly competitive US market. Employing a quantitative research methodology, the study utilized a multiple linear regression model to analyze data from 205 car models and 26 variables, with the analysis performed using RStudio. The findings indicated that significant predictors of car prices include city mileage per gallon (mpg), horsepower, fuel system, engine size, cylinder number, fuel type, and wheel drive type. Specifically, cars with an mpfi fuel system, five and twelve cylinders, rear-wheel drive, and gas fuel type were associated with higher profitability. The study provides valuable insights for automotive companies on optimizing car design and business strategies to achieve favorable pricing outcomes in the US market.

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.