In a study of 2005 model cars, a researcher computed the least squares regressionline of miles per gallon (mpg) on weight (in pounds). They obtained the followingequation for this line:mpg = 34.576 - 0.007weightBased on the least-squares line, we would predict a 2005 model car with weightequal to 3,000 pounds would have a mpg ofFill in the blank with: ##.###

Given data in a study of 2005 model cars a researcher computed the least squares regression…

Answered: In a study of 2005 model cars, a…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Consumers are often interested in the fuel efficiency of the vehicles they choose to buy, so much so that they will research the various models they consider buying. Fuel efficiency can depend on a variety of variables. In this analysis, there are 73 automobiles that are popular with consumers. A regression analysis has been performed; the dependent variable is CityMPG (EPA miles per gallon in city driving), and independent variables are Length (vehicle length in inches), Width (vehicle width in inches), Weight (vehicle weight in pounds), and ManTran (1 if manual shift transmission, 0 otherwise). The level of significance is 0.05. Use the following MegaStat output to answer questions about this regression analysis. a. State the regression equation. b. How would CityMPG be affected if the width of a vehicle increased by an inch? c. Estimate the CityMPG for a vehicle with a length of 190 inches, a width of 75 inches, a weight of 4100 pounds, and a manual. Round your answer to the nearest…
Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.8 277.9 253.3 259.2 279.0 258.0 274.0 262.4 Height 1784.7 1771.2 1676.0 1645.9 1858.9 1710.1 1788.9 1737.0 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.8 mm is enter your response heremm. (Round to the nearest integer as needed.)
19. You might think that increasing the resources available would elevate the number of plant spe- cies that an area could support, but the evidence suggests otherwise. The data in the accompany- ing table are from the Park Grass Experiment at Rothamsted Experimental Station in the U.K., where grassland field plots have been fertilized annually for the past 150 years (collated by Harpole and Tilman 2007). The number of plant species recorded in 10 plots is given in response to the number of different nutrient types added Plot 1 2 3 4 5 6 7 8 9 10 Number of nutrients added 0 0 0 3144 E2 3 Number of plant species 36 36 32 34 33 30 20 23 21 16
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 253.2 258.7 278.7 257.8 274.2 262.2 Height 1784.8 1771.0 1675.6 1645.9 1858.7 1710.1 1789.2 1737.4 the regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is enter your response here mm. (Round to the nearest integer as needed.)
Draw a graph of the least-squares regression line on your scatterplot. (For hand-drawing, round the slope and y-intercept to one decimal place before drawing the line.) Be sure to show how you were able to plot the line starting with its equation. Model City Miles per Gallon Highway Miles per Gallon Acura RLX 20 29 BMW 530i 24 34 Buick LaCrosse eAssist 25 35 Chevrolet Malibu 29 36 Ford Hybrid FWD 43 41 Honda Civic 32 42 Infiniti Q50 Red Sport 20 26 Kia Forte 30 40 Lexus ES 350 22 33 Mercedes Benz AMG S 21 30 Mini Cooper Clubman 24 32 Nissan Maxima 20 30 Suburu Legacy AWD 25 34 Toyota Prius ECO 58 53
A group of researchers measured how funny people rated a cartoon when they were in one of two groups: holding a pen in their teeth (forcing them to smile) or holding a pen in their lips (forcing them to frown). For this study, identify the independent variable and the dependent variable. Justify your response.
You are studying how a penguin's bill length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. Given the information below, choose the formula for the least squares regression line. b₁ = 87.42 bo = 362.31 x = 43.92 y = 4202.0 O Bill Length = 87.42 Body mass + 362.31 O Bill Length = 87.42*4202.0 + 362.31 O 4202.0 = 362.31*43.92 +87.42 O Body mass = 87.42 * Bill Length + 362.31 O Body mass = 362.31 *Bill Length + 87.42 O Body mass = 362.31 43.92 + 87.42
The data shows a systolic and a diastolic blood pressure of certain patients. Find the linear regression eqation, using the first variable (systolic) as the independent variable. Find the best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140. Use a significance level of x Systolic Diastolic 116 82 124 88 112 72 144 95 138 96 112 76 145 88 124 76 130 90 129 94 1)Find the correlation coefficient. 2)State the appropriate critical r value to use to test the claim that the correlation between systolic blood pressure and diastolic blood pressure is significantly different from 0 3)The correlation between diastolic blood pressure and systolic blood pressure is: significant, not significant, weak, none 4)The best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140 is closest to 1pts

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS