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Plot a scatter diagram for the data used in topic (c) and plot the linear regression model as well. Follow the same guidance provided in Problem #1. ., Based on the coefficient of determination (r) for each linear regression models developed, which of the two linear regression models should be used to predict the Final Grade in QMB 3600 and explain your decision.

Plot a scatter diagram for the data used in topic (c) and plot the linear regression model as well. Follow the same guidance provided in Problem #1. ., Based on the coefficient of determination (r) for each linear regression models developed, which of the two linear regression models should be used to predict the Final Grade in QMB 3600 and explain your decision.

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
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Just need the last 2 parts please. Starting at plot a scatter diagram & then the one below that based on.. Thank you (: 

**Students in QMB 3600 – Quantitative Methods in Business course** decided to research the relationship between the Mid-Term Exam, the average number of study hours spent per week during the semester, and the final course grade for a given student. A sample containing data from a previous semester was provided by the instructor and summarized in the following table: | Student # | Mid-Term Exam Grade | Average Number of Study Hours per week | Final Course Grade | |-----------|---------------------|---------------------------------------|--------------------| | 1 | 50.0 | 2.0 | 65.0 | | 2 | 60.0 | 4.0 | 85.0 | | 3 | 55.0 | 3.5 | 78.0 | | 4 | 85.0 | 6.0 | 90.0 | | 5 | 55.0 | 5.0 | 91.0 | | 6 | 72.0 | 4.5 | 89.0 | | 7 | 75.0 | 6.5 | 91.0 | | 8 | 45.0 | 3.0 | 65.0 | | 9 | 88.0 | 5.5 | 89.0 | | 10 | 90.0 | 7.5 | 96.0 | **Instructions:** 1. **Develop a linear regression model**, where Y is the Final Grade Course and X is the Mid-Term Exam. Calculate the coefficient of determination (r²), the coefficient of correlation (r), the variance (σ²) for the model, and apply Hypothesis Testing to test the significance of the linear model for α = 0.05 exactly as demonstrated in the Triple A Construction Example on page 121 of your textbook. Show all your calculations and follow the same guidance provided in Problem #1. 2. **Plot a scatter diagram** for the data used in topic (a) and plot the linear regression model as well. Follow the same guidance provided in Problem #1. 3. **Develop a linear regression model**, where Y is the Final Grade Course and X is the Average Number of Study Hours
Transcribed Image Text:**Students in QMB 3600 – Quantitative Methods in Business course** decided to research the relationship between the Mid-Term Exam, the average number of study hours spent per week during the semester, and the final course grade for a given student. A sample containing data from a previous semester was provided by the instructor and summarized in the following table: | Student # | Mid-Term Exam Grade | Average Number of Study Hours per week | Final Course Grade | |-----------|---------------------|---------------------------------------|--------------------| | 1 | 50.0 | 2.0 | 65.0 | | 2 | 60.0 | 4.0 | 85.0 | | 3 | 55.0 | 3.5 | 78.0 | | 4 | 85.0 | 6.0 | 90.0 | | 5 | 55.0 | 5.0 | 91.0 | | 6 | 72.0 | 4.5 | 89.0 | | 7 | 75.0 | 6.5 | 91.0 | | 8 | 45.0 | 3.0 | 65.0 | | 9 | 88.0 | 5.5 | 89.0 | | 10 | 90.0 | 7.5 | 96.0 | **Instructions:** 1. **Develop a linear regression model**, where Y is the Final Grade Course and X is the Mid-Term Exam. Calculate the coefficient of determination (r²), the coefficient of correlation (r), the variance (σ²) for the model, and apply Hypothesis Testing to test the significance of the linear model for α = 0.05 exactly as demonstrated in the Triple A Construction Example on page 121 of your textbook. Show all your calculations and follow the same guidance provided in Problem #1. 2. **Plot a scatter diagram** for the data used in topic (a) and plot the linear regression model as well. Follow the same guidance provided in Problem #1. 3. **Develop a linear regression model**, where Y is the Final Grade Course and X is the Average Number of Study Hours
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