Please label each part Part (h)Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.)O no outliersO (4710, 15)O (2685, 24)O (3700, 25)O (2790, 38)O (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 itwould 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 - ŷ) 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 coefficientcoefficient of determinationFind the new best fit line. (Round your answers to four decimal places.)ŷ =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 themodel 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.O O O OO O O O We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the tableshow the weights, in pounds, and fuel efficiency, measured in miles per gallon, for a sample of 12 vehicles.FuelWeightEfficiency268524256027266029279038300025341022364020370025388021390021406019471015Part (a)+ Part (b)O Part (c)+ Part (d)O 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 thenearest whole number.)%Part (f)O Part (g)For the vehicle that weighs 3000 pounds, find the residual (y - ŷ). (Round your answer to two decimal places.)Does the value predicted by the line underestimate or overestimate the observed data value?underestimateoverestimate

Hello! As you have posted more than 3 sub parts, we are answering the first 3 sub-parts. In case…

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

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

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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Matrices

‘Matrices’ is the plural form of the word matrix, and it is basically a spreadsheet in the form of a box. In mathematics, various functions can be carried out with matrices. Generally, a matrix comes in the shape of a square or rectangle. The elements are distributed horizontally in the form of rows, and vertically in the form of columns.

Question

Please label each part

Part (h)
Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.)
O no outliers
O (4710, 15)
O (2685, 24)
O (3700, 25)
O (2790, 38)
O (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 - ŷ) 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 =
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.
O O O O
O O O O

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
Part (a)
+ Part (b)
O Part (c)
+ Part (d)
O 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 (f)
O Part (g)
For the vehicle that weighs 3000 pounds, find the residual (y - ŷ). (Round your answer to two decimal places.)
Does the value predicted by the line underestimate or overestimate the observed data value?
underestimate
overestimate

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