Find the regression equation, letting the first variable be the predictor (x) variable. Find the best predicted Nobel Laureate rate for a country that has 78.4 Internet users per 100 people. How does itcompare to the country's actual Nobel Laureate rate of 1.7 per 10 million people?XClick the icon to view the data.Find the equation of the regression line.ŷ=+x(Round the constant to one decimal place as needed. Round the coefficient to threData tableInternet Users Per 10080.179.278.44582.938.489.588.879.183.352.776.557.179.493.194.864.557.767.394.184.686.678.5Nobel Laureates5.524.48.70.16.10.125.47.6912.81.912.63.31.511.325.53.11.91.73231.318.810.8

Answered: Find the equation of the regression…

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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Consider the following regression equation: y = -5.3 + 8.8x.What does 8.8 depict in the regression equation above? a. None of the options. b. The direction of the line. c. The point where x = 0. d. The y-intercept.
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
Use exponential regression to extrapolate the value of y when x = 6. Round your answer to the nearest whole number. X y ¹2 77 -1 65 0 56 1 47 2 40 3 34 (Record your answer in the numerical response box from left to right.)
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
b. Find the equation of the regression line for the minutes the actor appeared shirtless in a film and the film's opening weekend gross. y = ___x + ____(Round to two decimal places as needed)
Find the slope (b1) for the regression equation for the following values. Round to 3 decimal places. Define Variables xi yi 33 180 25 170 50 200 65 201 57 160 27 165
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=-0.926 b=22.103 r2=0.481636 r=-0.694 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)
Find the slope (b1) for the regression equation for the following values. Round to 3 decimal places. Define Variables xi yi 33 180 25 170 50 200 65 186 57 160 27 165
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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=a+bx b=-0.66 a=25.376 r²=0.390625 r=-0.625 Use this to predict the number of situps a person who watches 2.5 hours of TV can do. place. Round to one decimal
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
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Find the equation of the regression line. ŷ=+xx (Round the constant to one decimal place as needed. Round the coefficient to thre