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Jamison is investigating how long his phone's battery lasts (in hours) for various brightness levels (on a scale of 0-100). His data is displayed in the table and graph below. Brightness Level (x) Hours (y) 10 How's 9 8 7 6 ". 16 21 29 7.1 6.4 6.6 ● 67 84 39 46 62 3.8 4.9 4.6 4.5 1.6

Jamison is investigating how long his phone's battery lasts (in hours) for various brightness levels (on a scale of 0-100). His data is displayed in the table and graph below. Brightness Level (x) Hours (y) 10 How's 9 8 7 6 ". 16 21 29 7.1 6.4 6.6 ● 67 84 39 46 62 3.8 4.9 4.6 4.5 1.6

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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### Investigation of Phone Battery Life at Various Brightness Levels #### Overview: Jamison is investigating how long his phone's battery lasts (in hours) for various brightness levels (on a scale of 0-100). His collected data is displayed in both a table and a scatter plot below. #### Data Table: The table below represents the battery life of the phone at different brightness levels. | Brightness Level (x) | 16 | 21 | 29 | 39 | 46 | 62 | 67 | 84 | |-----------------------|-----|-----|-----|-----|-----|-----|-----|-----| | Hours (y) | 7.1 | 6.4 | 6.0 | 6.6 | 3.8 | 4.9 | 4.6 | 1.6 | #### Graph Description: The graph illustrates the relationship between brightness levels and battery life. The x-axis represents the brightness level ranging from 0 to 100, and the y-axis represents the battery life in hours, ranging from 0 to 10 hours. Blue dots on the graph represent the data points for various brightness levels. Key observations: - As the brightness level increases, the battery life tends to decrease. - The highest battery life is observed at the brightness level of 16, with the phone lasting 7.1 hours. - The shortest battery life is observed at the brightness level of 84, with the phone lasting only 1.6 hours. #### Example Problem: What is the residual for the point (67, 4.5)? Round to 4 decimal places. **Solution:** (The residual is the difference between the observed value and the predicted value from the linear regression model. This is an exercise for students to apply linear regression techniques to find the predicted value for the brightness level of 67 and then calculate the residual by subtracting the predicted value from 4.5.)
Transcribed Image Text:### Investigation of Phone Battery Life at Various Brightness Levels #### Overview: Jamison is investigating how long his phone's battery lasts (in hours) for various brightness levels (on a scale of 0-100). His collected data is displayed in both a table and a scatter plot below. #### Data Table: The table below represents the battery life of the phone at different brightness levels. | Brightness Level (x) | 16 | 21 | 29 | 39 | 46 | 62 | 67 | 84 | |-----------------------|-----|-----|-----|-----|-----|-----|-----|-----| | Hours (y) | 7.1 | 6.4 | 6.0 | 6.6 | 3.8 | 4.9 | 4.6 | 1.6 | #### Graph Description: The graph illustrates the relationship between brightness levels and battery life. The x-axis represents the brightness level ranging from 0 to 100, and the y-axis represents the battery life in hours, ranging from 0 to 10 hours. Blue dots on the graph represent the data points for various brightness levels. Key observations: - As the brightness level increases, the battery life tends to decrease. - The highest battery life is observed at the brightness level of 16, with the phone lasting 7.1 hours. - The shortest battery life is observed at the brightness level of 84, with the phone lasting only 1.6 hours. #### Example Problem: What is the residual for the point (67, 4.5)? Round to 4 decimal places. **Solution:** (The residual is the difference between the observed value and the predicted value from the linear regression model. This is an exercise for students to apply linear regression techniques to find the predicted value for the brightness level of 67 and then calculate the residual by subtracting the predicted value from 4.5.)
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