Multiple Linear Regression - Assignment Example

Download file to see previous pages It penalizes the model for including too many parameters that do not contribute much in explaining the original variance. It is a modification of R2.
3. Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated resulting to inter-associations among independent variables. This means that one can be linearly predicted from the rest that have non-tribal degree of accuracy. Multicollinearity is a problem because it makes the data unreliable.
Multicollinearity is measured using the variance inflation factor that assesses how much the variance of an estimated regression coefficient increases if the predictors are correlated, if not then the variance inflation factor becomes 1.
e. From the residual vs. fitted graph, the residuals appear randomly around zero line. This indicates that the assumption of linearity is reasonable. The normal q-q plot shows that the points re lined up on the identity line and thus, the dependent and independent variables are comparable. Scale-location plot shows that there is a downward trend in residuals. Cook’s distance’s graph shows that observation 4, 15 and 25 are influential in the model.
f. There are outliers in the variables Flux, East and South. Outliers in Flux are observation 19 and 25 which are ==40.6. Thus, we drop observations with outliers in East and South variables. Additionally, observation 4, 15 and 25 are considered to be influential in the dataset.
e. The residual vs. fitted graph shows that residuals appear randomly around the zero line. This indicates that the assumption of linearity is reasonable. The normal q-q plot shows that the points re lined up on the identity line and thus, the dependent and independent variables are comparable. Scale-location plot shows that there is a downward trend in ...Download file to see next pagesRead More