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Student's Final Grade

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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 5% significance level to conclude that the final grade and the number of late assignments are negatively 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 5% significance level to conclude that the final grade and the number of late assignments are negatively 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 5% significance level to conclude that the final grade and the number of late assignments are negatively 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 5% significance level to conclude that the final grade and the number of late assignments are negatively linearly related? ​ ​
-{Student's Final Grade Narrative} Does this data provide enough evidence at the 5% significance level to conclude that the final grade and the number of late assignments are negatively linearly related?

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blured image Rejection region: t < −t0.05,46≈−1.679 Test st...

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