Does how long young children remain at the lunch table help predict how much they eat? Twenty toddlerswere observed over several months at a nursery school. Time is the average number of minutes a childspent at the table when lunch was served. Calories is the average number of calories the child consumedduring lunch, calculated from careful observation of what the child ate each day. A regression was run, andthe results are as follows:ŷ = 178 +2.7xR²= 0.55; r0.743Predict the number of calories a toddler consumes who remains at the table for 13.5 minutes. Round youranswer to three decimal places, if necessary,=

From the provided information, The regression equation is as follow: y^=178+2.7x Here x is the…

Answered: Does how long young children remain at…

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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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.145 b=20.742 r?=0.508369 r=-0.713 Use this to predict the number of situps a person who watches 2.5 hours of TV can do (to one decimal place)
Does how long young children remain at the lunch table help predict how much they eat? Twenty toddlers were observed over several months at a nursery school. "Time" is the average number of minutes a child spent at the table when lunch was served. "Calories" is the average number of calories the child consumed during lunch, calculated from careful observation of what the child ate each day. A regression was run and the results were: = 114 + 2.4*x R² = 0.68 r = 0.822 Predict the number of calories a toddler consumes who remains at the table for 14.5 minutes (round your answer to two decimal places)
For Data Set 9 in Appendix B, “Bear Measurements,” we get this regression equation: Weight = -274 + 0.426 Length + 12.1 Chest Size, with R2 = 0.928. Interpret the multiple coefficient of determination – what does this value tell us?
A large school district is reevaluating its teachers' salaries. They have decided to use regression analysis to predict mean teacher salaries at each elementary school. The researcher uses years of experience to predict salary. The resulting equation was: Where Y=salary and X=years of experience. The raw data is given in the table below, Salary $54,000.00 $42,140.00 $36,195.00 $45,000.00 $58,950.00 $56,890.00 $53,250.00 $49,800.00 $35,820.00 $74,390,00 $28,900.00 $38,690.00 $78,070.00 $64,205.00 $20,000.00 Years of experience 13 9 7 9 14 13 14 11 8 22 5 8 23 18 2 a) Use Excel to estimate the regression equation (copy and paste your output). What is the coefficient on experience? b) What is the correlation coefficient? c) What is the coefficient of determination, and how do you interpret it? d) Discuss the overall significance of the model
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.013 b=27.906 r2=0.498436 r=-0.706 Use this to predict the number of sit ups a person who watches 12 hours of TV can do, and please round your answer to a whole number.
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…
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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
State the regression equation and use it to predict taxes for a house with lot size 10K.
A regression of average monthly expenditure (AME, measured in euros) on average monthly income (AMI, measured in euros) using a random sample of college-educated full-time workers earnings €100 to €1.5 million yields the following: AME = 710.7 + 8.8 × AMI, R2 = 0.030, SER = 540.30 d. What does the regression predict will be the expenditure of a person with an income of €100? With an income of €200? e. Will the regression give reliable predictions for a person with an income of €2 million? Why or why not? ( f. Given what you know about the distribution of earnings, do you think it is plausible that the distribution of errors in the regression is normal? (Hint: Do you think that the distribution is symmetric or skewed? What is the smallest value of earnings, and is it consistent with a normal distribution?).
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.)
The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive? Is the regression coefficient statistically significant? How can you tell?

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