Solved

Student's Final Grade

Question 119

Essay

Student's Final Grade
A statistics professor investigated some of the factors that affect an individual student's final grade in her course.She proposed the multiple regression model Student's Final Grade A statistics professor investigated some of the factors that affect an individual student's final grade in her course.She proposed the multiple regression model ,where y is the final grade (out of 100 points),x<sub>1</sub> is the number of lectures skipped,x<sub>2</sub> is the number of late assignments,and x<sub>3</sub> is the midterm exam score (out of 100).The professor recorded the data for 50 randomly selected students.The computer output is shown below. THE REGRESSION EQUATION IS ​ ​ ​ S = 13.74 R−Sq = 30.0% ​ ANALYSIS OF VARIANCE ​ ​ -{Student's Final Grade Narrative} Does this data provide enough evidence at the 1% significance level to conclude that the final grade and the midterm exam score are positively linearly related? ,where y is the final grade (out of 100 points),x1 is the number of lectures skipped,x2 is the number of late assignments,and x3 is the midterm exam score (out of 100).The professor recorded the data for 50 randomly selected students.The computer output is shown below. THE REGRESSION EQUATION IS Student's Final Grade A statistics professor investigated some of the factors that affect an individual student's final grade in her course.She proposed the multiple regression model ,where y is the final grade (out of 100 points),x<sub>1</sub> is the number of lectures skipped,x<sub>2</sub> is the number of late assignments,and x<sub>3</sub> is the midterm exam score (out of 100).The professor recorded the data for 50 randomly selected students.The computer output is shown below. THE REGRESSION EQUATION IS ​ ​ ​ S = 13.74 R−Sq = 30.0% ​ ANALYSIS OF VARIANCE ​ ​ -{Student's Final Grade Narrative} Does this data provide enough evidence at the 1% significance level to conclude that the final grade and the midterm exam score are positively linearly related? ​ Student's Final Grade A statistics professor investigated some of the factors that affect an individual student's final grade in her course.She proposed the multiple regression model ,where y is the final grade (out of 100 points),x<sub>1</sub> is the number of lectures skipped,x<sub>2</sub> is the number of late assignments,and x<sub>3</sub> is the midterm exam score (out of 100).The professor recorded the data for 50 randomly selected students.The computer output is shown below. THE REGRESSION EQUATION IS ​ ​ ​ S = 13.74 R−Sq = 30.0% ​ ANALYSIS OF VARIANCE ​ ​ -{Student's Final Grade Narrative} Does this data provide enough evidence at the 1% significance level to conclude that the final grade and the midterm exam score are positively linearly related? ​ ​ S = 13.74 R−Sq = 30.0% ​ ANALYSIS OF VARIANCE Student's Final Grade A statistics professor investigated some of the factors that affect an individual student's final grade in her course.She proposed the multiple regression model ,where y is the final grade (out of 100 points),x<sub>1</sub> is the number of lectures skipped,x<sub>2</sub> is the number of late assignments,and x<sub>3</sub> is the midterm exam score (out of 100).The professor recorded the data for 50 randomly selected students.The computer output is shown below. THE REGRESSION EQUATION IS ​ ​ ​ S = 13.74 R−Sq = 30.0% ​ ANALYSIS OF VARIANCE ​ ​ -{Student's Final Grade Narrative} Does this data provide enough evidence at the 1% significance level to conclude that the final grade and the midterm exam score are positively linearly related? ​ ​
-{Student's Final Grade Narrative} Does this data provide enough evidence at the 1% significance level to conclude that the final grade and the midterm exam score are positively linearly related?

Correct Answer:

verifed

Verified

blured image Rejection region: t > t0.01,46≈ 2.412 Test sta...

View Answer

Unlock this answer now
Get Access to more Verified Answers free of charge

Related Questions

Q114: If the value of the Durbin-Watson statistic,d,satisfies

Q115: An adverse effect of multicollinearity is that

Q116: The range of the values of the

Q117: When an additional explanatory variable is introduced

Q118: Life Expectancy<br> An actuary wanted to

Q120: When an additional explanatory variable is introduced

Q121: For a multiple regression model the following

Q122: To use the Durbin-Watson test to test

Q123: In a multiple regression analysis,if the model

Q124: A high value of the coefficient of