Amival Time (Minutes before 6 p.m.) 120 140 b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? There appears to be a i @ relationship between the two variables. Does there appear to be any oc:%a and/or influential observations? Observation E @ appears to be an observation with high leverage and may be very influential in terms of fitting a linear model to the data. c. Using the entire data set, develop the estimated regression equation that can be used to predict the shopping time given the arrival time (to 4 decimals). Y 0+ 0.& d. Use residual analysis to determine whether any outliers or influential observations are present. Observation E @ has a standardized residual i @. indicating it is an outlier. e. After looking at the scatter diagram in part (a), suppose you were able to visually identify what appears to be an influential observation. Drop this observation from the data set and fit an estimated regression equation to the remaining data (to 4 decimals). @H' . .Q.T ea Compare the es timated slope for the new estimated regression equation to the estimated slope obtained in part (c). Does this approach confirm the conclusion you reached in part (d)? Explain. The slope of the estimated regression equation is now | 8 as compared to a value of , 0 when this observation is included. Thus, we see that this observation I @ a impact on the value of the slope of the fitted line and hence we i @ that it is an influential observation