Critics of television often refer to the detrimental effects that all the violence shown on television has on children. However, there may be another problem. It may be that watching television also reduces the amount of physical exercise, causing weight gains. The following regression estimates show the relationship between the number of kilograms (in kgs) a child is overweight by, and the number of hours of television viewing per week overweight 0.55 + 0.13 hours %3D (0.27) (0.04) Standard errors are shown in the brackets. The sample size is N=1800. Based on the regression results, how much does overweight change if the average number hours spent watching television is increased by 2 hours? Increase by 0.26. Decrease by 0.13. Increase by 0.04. Increase by 0.13.

Critics of television often refer to the detrimental effects that all the violence shown on television has on children. However, there may be another problem. It may be that watching television also reduces the amount of physical exercise, causing weight gains. The following regression estimates show the relationship between the number of kilograms (in kgs) a child is overweight by, and the number of hours of television viewing per week overweight 0.55 + 0.13 hours %3D (0.27) (0.04) Standard errors are shown in the brackets. The sample size is N=1800. Based on the regression results, how much does overweight change if the average number hours spent watching television is increased by 2 hours? Increase by 0.26. Decrease by 0.13. Increase by 0.04. Increase by 0.13.

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 local retail store compared their monthly sales of umbrellas with the amount of rainfall that occured during that month. They computed the following statistics: Rainfall (in) # of umbrellas mean = 4.64 mean = 34.2 SD = 1.17 SD = 13.2 r = 0.8 1. Find the equation for the regression line that predicts the monthly sales of umbrellas from monthly rainfall.
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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.)
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