Please label each part SUBPART **Exploring Weight and Fuel Efficiency in Vehicles**We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the table show the weights, in pounds, and fuel efficiency, measured in miles per gallon, for a sample of 12 vehicles.| Weight (lbs) | Fuel Efficiency (mpg) ||--------------|-----------------------|| 2685 | 24 || 2560 | 27 || 2660 | 29 || 2790 | 38 || 3000 | 25 || 3410 | 22 || 3640 | 20 || 3700 | 25 || 3880 | 21 || 3900 | 21 || 4060 | 19 || 4710 | 15 |In this study, we aim to determine how the weight of a vehicle affects its fuel efficiency. Typically, lighter vehicles tend to have better fuel efficiency, but this data set will help us analyze specific trends and outliers in a small sample of vehicles. **Part (h)****Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.)**- [ ] no outliers- [ ] (4710, 15)- [ ] (2685, 24)- [ ] (3700, 25)- [ ] (2790, 38)- [ ] (4060, 19)---**Part (i)**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 is creating a curved least squares regression line.- ( ) The outlier does not lie directly on the line, but it is close.- ( ) The outlier represents a different population of vehicles compared to the rest.- ( ) The outlier lies directly on the line, so the error residual ($ y - \hat{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.)- $ \hat{y} = $ [______]x + [______]---**Part (j)**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 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 closer to zero.- ( ) The new linear model is a better fit, because the new correlation coefficient is farther from zero.- ( ) The first linear model is a better fit, because the first correlation coefficient is farther from zero.

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Description: Please label each part SUBPART **Exploring Weight and Fuel Efficiency in Vehicles**We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the table show the weights, in

Part-h: Using scatter plot we can find the outlier in the above data. To draw scatter plot using…

Math

Statistics

We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the table show the weights, in pounds, and fuel efficiency, measured in miles per gallon, for a sample of 12 vehicles. Fuel Weight Efficiency 2685 24 2560 27 2660 29 2790 38 3000 25 3410 22 3640 20 3700 25 3880 21 3900 21 4060 19 4710 15

We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the table show the weights, in pounds, and fuel efficiency, measured in miles per gallon, for a sample of 12 vehicles. Fuel Weight Efficiency 2685 24 2560 27 2660 29 2790 38 3000 25 3410 22 3640 20 3700 25 3880 21 3900 21 4060 19 4710 15

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Percentage

A percentage is a number indicated as a fraction of 100. It is a dimensionless number often expressed using the symbol %.

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In mathematics, an algebraic expression consists of constant(s), variable(s), and mathematical operators. It is made up of terms.

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Numbers are some measures used for counting. They can be compared one with another to know its position in the number line and determine which one is greater or lesser than the other.

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Before we begin to understand the subtraction of algebraic expressions, we need to list out a few things that form the basis of algebra.

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Question

Please label each part

SUBPART

**Exploring Weight and Fuel Efficiency in Vehicles**

We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the table show the weights, in pounds, and fuel efficiency, measured in miles per gallon, for a sample of 12 vehicles.

| Weight (lbs) | Fuel Efficiency (mpg) |
|--------------|-----------------------|
| 2685 | 24 |
| 2560 | 27 |
| 2660 | 29 |
| 2790 | 38 |
| 3000 | 25 |
| 3410 | 22 |
| 3640 | 20 |
| 3700 | 25 |
| 3880 | 21 |
| 3900 | 21 |
| 4060 | 19 |
| 4710 | 15 |

In this study, we aim to determine how the weight of a vehicle affects its fuel efficiency. Typically, lighter vehicles tend to have better fuel efficiency, but this data set will help us analyze specific trends and outliers in a small sample of vehicles.

**Part (h)**

**Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.)**

- [ ] no outliers
- [ ] (4710, 15)
- [ ] (2685, 24)
- [ ] (3700, 25)
- [ ] (2790, 38)
- [ ] (4060, 19)

---

**Part (i)**

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 is creating a curved least squares regression line.
- ( ) The outlier does not lie directly on the line, but it is close.
- ( ) The outlier represents a different population of vehicles compared to the rest.
- ( ) The outlier lies directly on the line, so the error residual ($ y - \hat{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.)

- $ \hat{y} = $ [______]x + [______]

---

**Part (j)**

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 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 closer to zero.
- ( ) The new linear model is a better fit, because the new correlation coefficient is farther from zero.
- ( ) The first linear model is a better fit, because the first correlation coefficient is farther from zero.

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