The analysis aims to study the effect of the perception of corruption among business executives (Q114) on the preference for private versus state ownership of businesses (Q107). By fitting both linear and generalized linear models, we seek to understand if and how perceptions of corruption influence individuals' opinions on business ownership models in Tunisia. The models also control for other covariates like urban/rural status (H_URBRURAL), interview date (J_INTDATE), age (Q262), and sex (Q260). The analysis reveals a significant positive association between the perception of corruption among business executives and the preference for government ownership of businesses. This relationship remains robust even when controlling for other variables, including urban/rural status, interview date, age, and sex. Specifically, individuals who perceive higher levels of corruption are more likely to prefer government ownership. Urban residents and older individuals also show a stronger preference for government ownership. Additionally, the data suggests that over time, preferences might be gradually shifting towards government ownership. These findings highlight the impact of corruption perceptions on ownership preferences and underscore the importance of addressing corruption to influence public opinion on economic policies.

Most_Streamed_Spotify_Songs_2024.csv

The objective of this project is to develop a predictive model that accurately forecasts the selling prices of used cars based on various features such as year of manufacture, kilometers driven, fuel type, seller type, transmission, ownership, mileage, engine capacity, and maximum power. By employing regression analysis techniques, this project aims to provide valuable insights into the factors that influence car prices and to create a reliable tool for predicting the market value of used cars.