- We Are Interested In Exploring The Relationship Between The Weight Of A Vehicle And Its Fuel Efficiency Gasoline Mileag 1 (39.94 KiB) Viewed 49 times
- We Are Interested In Exploring The Relationship Between The Weight Of A Vehicle And Its Fuel Efficiency Gasoline Mileag 2 (27.63 KiB) Viewed 49 times
- We Are Interested In Exploring The Relationship Between The Weight Of A Vehicle And Its Fuel Efficiency Gasoline Mileag 3 (19 KiB) Viewed 49 times
- We Are Interested In Exploring The Relationship Between The Weight Of A Vehicle And Its Fuel Efficiency Gasoline Mileag 4 (14.84 KiB) Viewed 49 times
- We Are Interested In Exploring The Relationship Between The Weight Of A Vehicle And Its Fuel Efficiency Gasoline Mileag 5 (32.2 KiB) Viewed 49 times
Part (d) Write the sentence that interprets the meaning of the slope of the line in the context of the data. For every one mile per gallon increase in fuel efficiency, the weight changes by the value of the slope. For every one car added to the data set, the average fuel efficiency will change by the value of the slope. For every one pound increase in weight, the fuel efficiency changes by the value of the slope. For every one car added to the data set, the average weight will change by the value of the slope. Part (e) What percent of the variation in fuel efficiency is explained by the variation in the weight of the vehicles, using the regression line? (Round your answer to the nearest whole number.) %
Part (1) Graph the best fit line on your scatterplot Fuel Efficiency 50 40 30 20 10 Fuel Efficiency 50 40 30 20 10 3000 3000 4000 4000 Weight 5000 Weight 5000 Weight: 5000 4000 3000 Weight 5000 4000 3000 10 20 30 10 20 30 40 40 Fuel 50 Efficiency Fuel 50 Efficiency
Part (g) For the vehicle that weighs 3000 pounds, find the residual (-9). (Round your answer to two decimal places.) Does the value predicted by the line underestimate or overestimate the observed data value? underestimate overestimate Part (h) Identify any outiers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.) (4710, 15) (2790, 38) no outlers (3700, 26) (2695, 25) (4060, 21)
Part() The outlier is a hybrid car that runs on gasoline and electric technology, but all other vehicles in the sample have engines that use gasoline only. Explain why it would be appropriate to remove the outlier from the data in this situation. The outlier does not lie directly on the line, but it is close. The outlier is creating a curved least squares regression line. The outlier represents a different population of vehicles compared to the rest. The outlier lies directly on the line, so the error residual (y->) is zero. Remove the outlier from the sample data. Find the new correlation coefficient and coefficient of determination. (Round your answers to two decimal places.) correlation coefficient coefficient of determination Find the new best fit line. (Round your answers to four decimal places.) ŷ= Part() Compare the correlation coefficients and coefficients of determination before and after removing the outlier, and explain what these numbers indicate about how the model has changed. The first linear model is a better fit, because the first correlation coefficient is closer to zero. The new linear model is a better fit, because the new correlation coefficient is farther from zero. The new linear model is a better fit, because the new correlation coefficient is closer to zero. The first linear model is a better fit, because the first correlation coefficient is farther from zero,