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 of272.7 mm. How does the result compare to the actual height of 1776 mm?Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7Height1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0The regression equation is ŷ = + (x.(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 272.7 mm is(Round to the nearest integer as needed.)How does the result compare to the actual height of 1776 mm?O A. The result is close to the actual height of 1776 mm.O B. The result is exactly the same as the actual height of 1776 mm.O C. The result is very different from the actual height of 1776 mm.O D. The result does not make sense given the context of the data.Cmm.

Answered: Image /qna-images/answer/df759258-87f1-4562-904b-8e650c5c069d.jpg

Answered: Foot Length 282.3 277.8 252.8 258.7…

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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Emily is a fifth-grade student who completed a standardized reading test. She scored one standard deviation above the mean score. Answer the following questions: How does the normal curve help you understand what this means about how Emily compared to other children who took the test? Explain how you determined your findings. How many children scored lower than Emily? How many children scored higher?
The table gives the amount of money (in dollars) spent on football by a major university. Letting a represent the number of years since 2005, and letting y represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2005 2006 2007 2008 2009 2010 Dollars spent on football 165,000 196,000 210,000 226,000 242,000 279,000 The equation of the line of best fit is: Hint Predict the amount of money that will be spent on football in the year 2024. +A Predict the amount of money that will be spent on football in the year 2041.
10:56 AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-squares regressio. OC OO D The managers of an electric utility wish to examine the relationship between temperature and electricity use in the utility's service region during the summer months. In particular, the managers wish to be able to predict total electricity use for a day from the maximum temperature that day. The bivariate data below give the maximum temperature (in degrees Fahrenheit) and the electricity use (in thousands of kilowatt hours) of electricity generated and sold for a random sample of sixteen summer days. A best-fitting line for the data, obtained from least- squares regression, is given by =53.28 + 3.07.x, in which x denotes the maximum temperature and y denotes the electricity use. This line is shown in the Figure 1 scatter plot. Temperature, x Electricity use, y (in degrees Fahrenheit) (in thousands of kilowatt hours) 78.4 295.4 72.5 232.1 00- 72.7 276.8 94.5 363.4 375 94.8 335.6…
Use the computer output to estimate the interceptPo and the slope B,. The regression equation is Y 797 - 4.73X. %3D Predictor Coef SE Coef T Constant 797.101 86.64 9.20 0.000 -4.731 1.551 -3.05 0.006 Intercept Bo: Slope B: eTextbook and Media Hint Save for Later Type here to search
X= 69-67- 67 -64-48-69 -58-74-70 Y= 10-95-10 -9-9.5- U-6- 11.5- l0.5 S CATTER PLOT Y 14 12 SHOE SIZE 6. 4 20 30 40 50 60 70 80 HEIGT c. Write the equation of the regression line and use it to predict the shoe size of a person that is 68 inches tall.
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=-0.849 b=32.064 r2=0.614656 r=-0.784 Use this to predict the number of situps a person who watches 12 hours of TV can do (to one decimal place)
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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: ý = bo + bịx bo = 36.465 - 1.082 b1 - 0.646 Use this to predict the number of situps a person who watches 2.5 hours of TV can do (to one decimal place)
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=-0.609 b=27.646 r2=0.910116 r=-0.954 Use this to predict the number of situps a person who watches 12 hours of TV can do (to one decimal place)
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=a+bx a=20.222 b=-0.911 r2=0.966289 r=-0.983 Use this to predict the number of situps a person who watches 10 hours of TV can do (to three decimal places) situps
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=a+bx a=28.724 b=-1.299 r2=0.388129 r=-0.623 Use this to predict the number of situps a person who watches 7 hours of TV can do (to one decimal place) Submit Question
The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…

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