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6.3 7) A college professor was becoming annoyed by how many of his students were absent during his 8:00 a.m. section of Philosophy 103. He decided to analyze whether these absences were impacting student scores. He assigned his TA the task of keeping track of attendance. At the end of the semester he compared each student's grade on the final exam (100 points possible) with the number of times he or she had been absent. His findings are displayed in the graph to the right. a) Identify the explanatory and response variables. b) Describe the relationship between these two variables. (Form, Direction, Strength, Outliers) O 10 c) Jeremy was absent 25 times. What would you predict his score on the final exam to be? d) Lucy often overslept and missed 43 class sessions. What would you predict for her score on the final? Grade on Final Exam 88229991 20 10 D Absences & Final Exam Scores 9-91.704 -1.654x R²=0.8732 Number of Absences (87 total days)

6.3 7) A college professor was becoming annoyed by how many of his students were absent during his 8:00 a.m. section of Philosophy 103. He decided to analyze whether these absences were impacting student scores. He assigned his TA the task of keeping track of attendance. At the end of the semester he compared each student's grade on the final exam (100 points possible) with the number of times he or she had been absent. His findings are displayed in the graph to the right. a) Identify the explanatory and response variables. b) Describe the relationship between these two variables. (Form, Direction, Strength, Outliers) O 10 c) Jeremy was absent 25 times. What would you predict his score on the final exam to be? d) Lucy often overslept and missed 43 class sessions. What would you predict for her score on the final? Grade on Final Exam 88229991 20 10 D Absences & Final Exam Scores 9-91.704 -1.654x R²=0.8732 Number of Absences (87 total days)

MATLAB: An Introduction with Applications
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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### Understanding the Impact of Attendance on Student Performance #### Problem Statement A college professor was becoming annoyed by how many of his students were absent during his 8:00 a.m. section of Philosophy 103. He decided to analyze whether these absences were impacting student scores. He assigned his TA the task of keeping track of attendance. At the end of the semester he compared each student’s grade on the final exam (100 points possible) with the number of times he or she had been absent. His findings are displayed in the graph to the right. #### Analysis Tasks a) **Identify the explanatory and response variables.** b) **Describe the relationship between these two variables.** - **Form** - **Direction** - **Strength** - **Outliers** c) **Jeremy was absent 25 times. What would you predict his score on the final exam to be?** d) **Lucy often overslept and missed 43 class sessions. What would you predict for her score on the final?** #### Graph Analysis: Absences & Final Exam Scores - **Title**: Absences & Final Exam Scores - **X-Axis**: Number of Absences (87 total days) - **Y-Axis**: Grade on Final Exam The scatter plot presents the grades of students on the final exam as a function of the number of absences. There is a negatively sloped trend line represented by the equation \( \hat{y} = 91.704 - 1.654x \) with an \( R^2 \) value of 0.8732. **Key Observations**: - **Form**: The relationship appears to be linear, as indicated by the straight-line trend. - **Direction**: The trend is negative, implying that more absences are associated with lower final exam scores. - **Strength**: With an \( R^2 \) value of 0.8732, the data points show a strong fit to the trend line, indicating a strong linear relationship between absences and final exam scores. - **Outliers**: There do not appear to be significant outliers that deviate from the trend considerably. Using the trend line equation \( \hat{y} = 91.704 - 1.654x \): - **For Jeremy (25 absences)**: \[ \hat{y} = 91.704 -
Transcribed Image Text:### Understanding the Impact of Attendance on Student Performance #### Problem Statement A college professor was becoming annoyed by how many of his students were absent during his 8:00 a.m. section of Philosophy 103. He decided to analyze whether these absences were impacting student scores. He assigned his TA the task of keeping track of attendance. At the end of the semester he compared each student’s grade on the final exam (100 points possible) with the number of times he or she had been absent. His findings are displayed in the graph to the right. #### Analysis Tasks a) **Identify the explanatory and response variables.** b) **Describe the relationship between these two variables.** - **Form** - **Direction** - **Strength** - **Outliers** c) **Jeremy was absent 25 times. What would you predict his score on the final exam to be?** d) **Lucy often overslept and missed 43 class sessions. What would you predict for her score on the final?** #### Graph Analysis: Absences & Final Exam Scores - **Title**: Absences & Final Exam Scores - **X-Axis**: Number of Absences (87 total days) - **Y-Axis**: Grade on Final Exam The scatter plot presents the grades of students on the final exam as a function of the number of absences. There is a negatively sloped trend line represented by the equation \( \hat{y} = 91.704 - 1.654x \) with an \( R^2 \) value of 0.8732. **Key Observations**: - **Form**: The relationship appears to be linear, as indicated by the straight-line trend. - **Direction**: The trend is negative, implying that more absences are associated with lower final exam scores. - **Strength**: With an \( R^2 \) value of 0.8732, the data points show a strong fit to the trend line, indicating a strong linear relationship between absences and final exam scores. - **Outliers**: There do not appear to be significant outliers that deviate from the trend considerably. Using the trend line equation \( \hat{y} = 91.704 - 1.654x \): - **For Jeremy (25 absences)**: \[ \hat{y} = 91.704 -
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