c. Use the least squares method to develop the estimated regression equation (to 5 decimals). = x + d. For every additional $1000 in home value, estimate how much additional will be spent on landscaping (to 2 decimals). $ e. Use the equation estimated in part (c) to predict the landscaping expenditures for a home valued at $575,000 (to the nearest whole number). b. Compute the standardized residuals for these data (to 2 decimals, if necessary). Enter negative values as negative numbers. Observation 1 Observation 2 Observation 3 Observation 4 Observation 5 Observation 6 Observation 7 Observation 8 Do the data include any outliers? - Select your answer - c. Compute the leverage values for these data (to 2 decimals). Enter negative values as negative numbers. Observation 1 Observation 2 Observation 3 Observation 4 Observation 5 Observation 6 Observation 7 Observation 8 Does there appear to be any influential observations in these data? - Select your answer - b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? There appears to be a - Select your answer - relationship between the two variables. Does there appear to be any outliers and/or influential observations? Observation - Select your answer 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). + x d. Use residual analysis to determine whether any outliers or influential observations are present. Observation - Select your answer - has a standardized residual - Select your answer - 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). + x Compare the estimated 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 as compared to a value of when this observation is included. Thus, we see that this observation - Select your answer - a impact on the value of the slope of the fitted line and hence we - Select your answer - that it is an influential observation. b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? Negative relationship c. Develop the estimated regression equation that could be used to predict the price ($1000s) (to 4 decimals). = 16.469 d. Test for a significant relationship at the 0.05 level of significance (to 3 decimals). p-value = less than = 0.05. e. Did the estimated regression equation provide a good fit? Yes f. Provide an interpretation for the slope of the estimated regression equation (to 4 decimals but dollar value to the nearest cent). Enter negative values as negative numbers. The slope of the estimated regression is decrease Therefore, every additional 1,000 miles on the car's odometr will result in a $ in the predicted price. g. Suppose that you are considering purchasing a previously owned 2007 Camry that has been driven 60,000 miles. Using the estimated regression equation developed in part (c), predict the price for this car (round to nearest dollar). $ Is this the price you would offer the seller? No