Abstract: This project I did aimed at providing reliable estimates for comparing vehicles in 2016. The purpose is to help car buyers choose the most fuel-efficient vehicle that meets their needs. Using the linear regression knowledge to analysis the correlation between each factor(vehicles's weight, cylinder, engine size, etc.) and response(fuel economy).
Abstract: We consider a linear regression model and propose an omnibus test to simultaneously check the assumption of independence between the error and the predictor variables and the goodness of fit of the linear regression model.
Abstract:In the talk, I discuss the statistical analysis on the dataset “wine”. First I converted the numerical predictors into factorial ones and used the method which is similar to the factorial design to find the impact of different factors.Then making use of the algorithms of LDA and QDA I analyzed the data. The result of the computation shows a clear tendency on the means of the predictors that stand out as significant in the result of the factorial model. The data analysis suggests that 4 predictors are significant, whereas the other 7 are not.