Given that9Practice q 88. 1292s² = 100, s² = 81, Σ(x − x)(y – ÿ) = 480y)and n=8, the slope of the best-fitting regression line is:0.8410.6376.750.684None of these

Given : sx2=100∑x-x¯y-y¯=480n=8

Answered: Given that Practice q 88. 129 s² = 100,…

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=-1.307 b=32.392 r²=0.675684 r=-0.822 Use this to predict the number of situps a person who watches 12.5 hours of TV can do (to one decimal place)
Interpret the intercept and the coefficients of D1 and D2 in the regression above.
A statistics professor wants to use the number of hours a student studies for a statistic final exam (x) to predict the final exam score (y). A regression model was fit based on data collected for a class during the previous semester, with the following results: y =35.0 + 3x Which of the following is the correct interpretation of the regression coefficient (slope)? Select the correct response: When the student does not study for the final exam, the mean final exam score is 35.0. None of the above are an interpretation of the slope For each increase of one hour in studying time, the mean change in the final exam score is predicted to be 35.0 For each increase of one hour in studying time, the mean change in the final exam score is predicted to be 3.0.
A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: y=ax+b a=5.865 b=25.06 r²=0.81 r=0.9 Use this to predict the final exam score of a student who studies 4.5 hours per week, 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=-0.982 b=31.084 r2=0.471969 r=-0.687 Use this to predict the number of situps a person who watches 13.5 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=-1.383 b=24.599 r2=0.962361 r=-0.981 Use this to predict the number of situps a person who watches 5.5 hours of TV can do (to one decimal place)
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
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=-0.666 b=36.053 r2=0.552049 r=-0.743 Use this to predict the number of situps a person who watches 4.5 hours of TV can do (to one decimal place)
The following results were obtained from a simple linear regression analysis. Total sum of square = 5.7640. Unexplained sum of square = 0,2225. The coefficient of determination is: OA 0.0386 O B, 0.0402 OC0.9614 O D.0.9805

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Given that Practice q 88. 129 s² = 100, s²=81, Σ(x − x)(y- y) = 480 and n=8, the slope of the best-fitting regression line is: 0.841 0.637 6.75 0.684 None of these