ste this1:3338%KA pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. Sherandomly selects 11 children from her practice, measures their heights and head circumferences, and obtains theaccompanying data. Complete parts (a) through (g) below.Click the icon to view the children's data.(a) Find the least-squares regression line treating height as the explanatory variable and head circumference as theresponse variable.Data Tabley = X +(Round the slope to three decimal places and round the constant to one decimal place as needed.)View an exampleHeight (inches), X Head Circumference (inches), y D27.7517.824.7517.325.7517.426.2517.72517.128.2517.926.7517.52717.7262627.5|||=PrintGet more help.(...)17.517.717.81LTE2 + ...DoneOXClear allCheck answer (a) Find the least-squares regression line treating height as the explanatory variable and head circumference as theresponse variable.y = 0.163 x + (12.9)(Round the slope to three decimal places and round the constant to one decimal place as needed.)(b) Interpret the slope and y-intercept, if appropriate.First interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete yourchoice.A. For a height of 0 inches, the head circumference is predicted to be(Round to three decimal places as needed.)OB. For a head circumference of 0 inches, the height is predicted to be(Round to three decimal places as needed.)OD. For every inch increase in head circumference, the height increases by(Round to three decimal places as needed.)OE. It is not appropriate to interpret the slope.C. For every inch increase in height, the head circumference increases by 0.163 in., on average.(Round to three decimal places as needed.)A. For every inch increase in head circumference, the height increases by(Round to one decimal place as needed.)Interpret the y-intercept, if appropriate. Select the correct choice below and, if necessary, fill in the answer box tocomplete your choice.OC. For every inch increase in height, the head circumference increases by(Round to one decimal place as needed.)OB. For a height of 0 inches, the head circumference is predicted to be in.(Round to one decimal place as needed.)OD. For a head circumference of 0 inches, the height is predicted to be in.(Round to one decimal place as needed.)E. It is not appropriate to interpret the y-intercept.18-(c) Use the regression equation to predict the head circumference of a child who is 24.25 inches tall.y = 16.91¹ in.(Round to two decimal places as needed.)16-(d) Compute the residual based on the observed head circumference of the 24.25-inch-tall child in the table. Is thehead circumference of this child above or below the value predicted by the regression model?24The residual for this observation is 0.11, meaning that the head circumference of this child is below the valuepredicted by the regression model.(Round to two decimal places as needed.)Residual(e) Draw the least-squares regression line on the scatter diagram of the data and label the residual from part (d).Choose the correct graph below.Qin.18-in.16+24ResidualQC.18-in., on average.16-24Residualin., on average.in., on average.> D.A. No-this height is outside the scope of the model.OB. No-this height is not possible.C. Yes the calculated model can be used for any child's height.D. Yes this height is possible and within the scope of the model.E. More information regarding the child is necessary to make the decision.18-16-24Residual1(f) Notice that two children are 26.5 inches tall. One has a head circumference of 17.2 inches; the other has a headcircumference of 17.4 inches. How can this be?A. There is no logical explanation for this the two observations in question should have had the same headcircumference.B. The only explanation is that the difference was caused by measurement error.C. For children with a height of 26.5 inches, head circumferences vary.D. The only explanation is that the difference is due to the fact that one observation was of a boy, and oneobservation was of a girl.(g) Would it be reasonable to use the least-squares regression line to predict the head circumference of a child whowas 32 inches tall? Why?

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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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An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y=nothingx+(nothing) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) A. A weightless car will get nothing miles per gallon, on average. It is not appropriate to interpret the slope. B. For every pound added to the weight of the car, gas mileage in the city will decrease by nothing mile(s) per gallon, on…
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage. v. The une ory tor ine most recent m odel year. Complete parts (a) through (d) accompanying data represent the weights of various domestic cars and their miles per gallon in BEE Click here to view the weight and gas mileage data (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y = 0.00702 * + (43.2) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice.answer trom part a to find this answer O A. For every pound added to the weight of the car, gas mileage in the city will decrease by mile(s) per gallon, on average. A weightless car will get miles per gallon, on averag B. For every pound added to the weight of the car, gas…
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. LOADING... Click here to view the weight and gas mileage data. Question content area bottom Part 1 (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. ModifyingAbove y with caretyequals=negative 0.00756−0.00756xplus+(44.844.8) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) Part 2 (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) A. For every pound added to the weight of the car, gas mileage in the…
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
(b)
KidsFeet Regression Line y-Intercept The KidsFeet dataframe contains data collected on 39 fourth grade students in Ann Arbor, MI, in October 1997. Two of the measurements taken on the children were the length in centimeters, (length), and width in centimeters, (width), of their longest foot. This data could be used to answer the following Research Question: How is the width of a fourth-grade student's foot related to the length? Which of the following is the y-intercept of the regression line? ## ## Simple Linear Regression## ## Correlation coefficient r = 0.6411 ## ## Equation of Regression Line:## ## length = 9.817 + 1.658 * width ## ## Residual Standard Error: s = 1.025 ## R^2 (unadjusted): R^2 = 0.411 ( ) 1.0248 ( ) 1.6576 ( ) 22.8153 ( ) 0.411 ( ) 9.8172 ( )0.6411
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. LOADING... Click here to view the weight and gas mileage data. Question content area bottom Part 1 (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. ModifyingAbove y with caretyequals=enter your response herexplus+(enter your response here) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.)
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…
A least squares regression model performed to predict the selling price of houses found the following equation: Pricê = 169.328+35.3Area + 0.718Lotsize - 6543Age where Price is in dollars, Area is in square feet, Lotsize is in square feet, and Age is in years. The R2 is 0.92. One of the following interpretations is correct. Which is it? Explain why. a.) Each year a house Ages, it is worth $6543 less. b.) Every extra square foot of Area is associated with an additional $35.30 in average price, for houses with a given Lotsize and Age. c.) Every dollar in price means Lotsize increases 0.718 square feet. d.) This model fits 92% of the data points exactly.
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.
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. Car Weight and MPG (Round the x coefficient to five decimal places as needed. Round the constant to two decimal places as needed.) Weight (pounds), x Miles per Gallon, y 3806 16 3796 15 2669 24 3520 18 3361 21 2911 22 3808 18 2612 24 3375 19 3737 16 3320 19
Statistical software is generally used to test the significance of a regression model. The table below shows software output for a least squares regression line. Fill in the blanks with the correct answers, using the table above. Assume a significance level of 0.05. The variable "unemp" is the_______variable. The value of the slope of the regression line is_______. The slope of the regression line is not significantly different from______because the p-value is______ which is larger than 0.05.

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