The estimated regression equation for a model involving two independent variables and 10 observations follows:\[\hat{y} = 33.0798 + 0.6071x_1 + 0.7058x_2\]**a. Interpret \(b_1\) and \(b_2\) in this estimated regression equation (to 4 decimals).**- \(b_1 =\) (input box)(Selection menu for interpretation)Options:- \(y\) changes by 0.6071 when \(x_1\) increases by 1 unit and \(x_2\) stays the same- \(y\) changes by 0.6071 when \(x_2\) increases by 1 unit and \(x_1\) stays the same- \(y\) changes by 0.7058 when \(x_1\) increases by 1 unit and \(x_2\) stays the same- \(y\) changes by 0.7058 when \(x_2\) increases by 1 unit and \(x_1\) stays the same- \(b_2 =\) (input box)### Explanation:This regression equation involves two independent variables, \(x_1\) and \(x_2\). The coefficients \(b_1\) and \(b_2\) represent the expected change in the dependent variable \(y\) for a one-unit increase in each respective independent variable, holding the other variable constant. The given options allow selection for correct interpretation of these coefficients. **Estimated Regression Equation for Two Independent Variables**The estimated regression equation for a model involving two independent variables and 10 observations is as follows:\[\hat{y} = 33.0798 + 0.6071x_1 + 0.7058x_2\]### a. Interpretation of Coefficients \(b_1\) and \(b_2\)Interpret \(b_1\) and \(b_2\) in this estimated regression equation (to 4 decimals).\(b_1 = \) - [Select your answer -]\(b_2 = \) - [Select your answer -]### b. EstimationEstimate \(y\) when \(x_1 = 180\) and \(x_2 = 310\) (to 3 decimals).\[\]

Answered: Image /qna-images/answer/f05cbf95-cb1b-4a33-a42d-d7b4b553b2f8.jpg

Answered: The estimated regression equation for a…

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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The regression equation relating dexterity scores (x) and productivity scores (y) for the employees of a company is ŷ=5.43+3.43x. Ten pairs of data were used to obtain the equation. The same data yield r=0.383 and ȳ=55.42. What is the best predicted productivity score for a person whose dexterity score is 30 (round to the nearest hundredth)?
The estimated regression equation for a model involving two independent variables and 10 observations follows. ŷ = 32.4394 + 0.5695x1 + 0.7347x2 a. Interpret b1 and b2 in this estimated regression equation (to 4 decimals). b. Estimate y when x1 = 180 and x2 = 310 (to 3 decimals).
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=-0.634 b=30.709 r2=0.948676 r=-0.974 Use this to predict the number of situps a person who watches 10.5 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=-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)
= In a statistics course, a linear regression equation was computed to predict the final-exam score from the score on the first test. The equation as y 10+ 0.75x where y is the final-exam score and x is the score on the first test. Parvez scored 80 on the first test. What is the predicted value of the Parvez's score on the final exam? Parvez scored 60 on the final exam. What is the value of the residual?
The linear regression equation for predicting systolic blood pressure from age is as follows: y = 42 + 2.5x . Find the residual for a person who is 21 years of age with a systolic blood pressure of 91.9.
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=-1.375 b=26.852 r2=0.395641 r=-0.629 Use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place)
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=-1.223 b=22.907 r2=0.600625 r=-0.775 Use this to predict the number of sit ups a person who watches 5 hours of TV can do, and please round your answer to a whole number.
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)
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=-1.226 b=33.622 r²-0.4489 r=-0.67 Use this to predict the number of situps a person who watches 3 hours of TV can do (to one decimal place)
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=-1.152 b=30.418 r2=0.703921 r=-0.839 1. Use this to predict the number of sit ups a person who watches 2.5 hours of TV can do, and please round your answer to a whole number
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last 7 years. Construct and interpret a 98% prediction interval for the amount of oil imported by the country and the amount of oil produced by the country is 5,483 thousand barrels per day. The equation of the regression line is y = -1.194x + 16,234.906

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