2. What is the use of the regression line?C. A student conducted a regression analysis between the math grades of his classmates and thenumber of times they were absent in the subject. He found that the regression line that willpredict the grade (y) if the number of absences (x) is known=97.732 2.61x.ances?absences?457conces?1533?4. What is the predicted grade of a student who has 1 absence?5. What is the predicted grade of a student who has 0 absences?

The given regression equation is y' = 97.732-2.61x.

Answered: C. A student conducted a 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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A regression was run to determine if there is a relationship between hours of study per week (�) and the test scores (�).The results of the regression were:y=ax+b a=6.254 b=33.41 r2=0.976144 r=0.988 Use this to predict the final exam score of a student who studies 3 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 the number of sit-ups a person can do (y). The results were: y = a+bx b = -0.89 a = 23.49 r² = 0.6519 r = -0.8074 a. If a person watches 7 hours of television a day, predict how many sit-ups he can do. b. If a person can do 4 sit ups, predict how many hours of television a day they watch. hours
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.
The multiple regression describes how the mean value of y is related to the xi independent variables. The parameters ?i are used to describe how the mean value of y changes for a one-unit increase in xi when the other variables are held constant. The given estimated regression equation follows where x1 is the high-school grade point average, x2 is the SAT mathematics score, and y is the final college grade point average. ŷ = −1.38 + 0.0232x1 + 0.00482x2 If the variable x2 is held constant, then only changes in x1 will impact the predicted values of ŷ. Since the coefficient of x1 is positive, for each one-unit increase of x1, the values of ŷ will increase by the value of ?1, where ?1 = . In context, for each one point increase of the high-school grade point average, the final college grade point average will increase by this amount when the SAT mathematics score does not change. If the variable x1 is held constant, then only changes in x2 will impact the predicted values of ŷ. Since the…
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=-1.113 b=31.204 r2=0.866761 r=-0.931 Use this to predict the number of situps a person who watches 9.5 hours of TV can do (to one decimal place)
4)
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day () and number of sit ups a person can do (y). The results of the regression were: y=ax+b a=-1.153 b=22.899 r2=0.649636 r=-0.806 Use this to predict the number of sit ups a person who watches 8 hours of TV can do, and please round your answer to a whole number.
For 50 students of a class the regression equation of marks in Statistics (X) on the marks in Accountancy (Y) is 3Y – 5 X + 180 = 0. The mean marks of Accountancy is 44 and variance of marks in Statistics is 9/16th of the variance of marks in Accountancy. Find the mean marks of Statistics and the coefficient of correlation between marks in two subjects.
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=-1.075 b=27.958 r2=0.919681 r=-0.959 Use this to predict the number of situps a person who watches 12 hours of TV can do (to one decimal place)

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