Earned runWins, xaverage, y202.67183.31172.66163.72143.93124.39113.799.5.14 The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseballpitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regressionequation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not.(a) x = 5 wins(b) x = 10 wins(c) x = 19 wins(d) x = 15 winsE Click the icon to view the table of numbers of wins and earned run average.The equation of the regression line is y =_x+]:%3D(Round to two decimal places as needed.)

Given : X Y 20 2.67 18 3.31 17 2.66 16 3.72 14 3.93 12 4.39 11 3.79 9 5.14

Answered: 3 Click the icon to view the table of…

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=-1.309 b=28.368 r2=0.4096 r=-0.64 Use this to predict the number of situps a person who watches 3 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=-1.09 b=30.988 r2=0.458329 r=-0.677 Use this to predict the number of situps a person who watches 10 hours of TV can do (to one decimal place)
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…
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Please only answer “d” and “e” as I already have the other parts filled out. Thank you!
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3 Click the icon to view the table of numnbers of wins and earned run average. ..... The equation of the regression line is y = (Round to two decimal places as needed.)