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1. What is the explanatory variable and what is the response variable? 2. Create a scatter plot. Be sure to include labels and units. 3. Does it appear that there is a linear relationship between the mean number of cigarettes smoked per day and longevity? Is there a positive association or negative association? 4. Compute the linear regression equation and the correlation coefficient. 5. Interpret the slope and y-intercept in the context of cigarette smoking and longevity. 6. Use your model to predict the longevity for a man who smokes an average of 11 cigarettes per day. 7. Is the value you calculated in #6 an over or underestimation?. What is the residual? 8. Circle the point on your scatterplot that has the largest residual. 9. Would it be appropriate to try to predict the age of death of a man who smokes an average of 75 cigarettes per day? Why or why not. Use appropriate vocabulary in your answer. 10. Conduct a hypothesis test to determine whether the number of cigarettes is a good predictor of longevity. Include all the relevant steps. 11. Compute the 95% confidence interval for beta, the slope of the true regression equation Weite

1. What is the explanatory variable and what is the response variable? 2. Create a scatter plot. Be sure to include labels and units. 3. Does it appear that there is a linear relationship between the mean number of cigarettes smoked per day and longevity? Is there a positive association or negative association? 4. Compute the linear regression equation and the correlation coefficient. 5. Interpret the slope and y-intercept in the context of cigarette smoking and longevity. 6. Use your model to predict the longevity for a man who smokes an average of 11 cigarettes per day. 7. Is the value you calculated in #6 an over or underestimation?. What is the residual? 8. Circle the point on your scatterplot that has the largest residual. 9. Would it be appropriate to try to predict the age of death of a man who smokes an average of 75 cigarettes per day? Why or why not. Use appropriate vocabulary in your answer. 10. Conduct a hypothesis test to determine whether the number of cigarettes is a good predictor of longevity. Include all the relevant steps. 11. Compute the 95% confidence interval for beta, the slope of the true regression equation Weite

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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The table below has information about a sample of 15 men over the age of 50. The average number of cigarettes they smoked per day and their longevity (age at death) is included. Use the data to complete the following tasks. The numbers in parentheses are the points possible for each task. Subject mean number of 1 2 3 5 6 7 8 9 10 11 12 13 14 15 cigarettes smoked per day 4 23 25 38 17 8 4 26 11 19 14 35 29 23 longevity 80 78 60 53 85 84 73 79 81 75 68 72 58 92 65
Transcribed Image Text:The table below has information about a sample of 15 men over the age of 50. The average number of cigarettes they smoked per day and their longevity (age at death) is included. Use the data to complete the following tasks. The numbers in parentheses are the points possible for each task. Subject mean number of 1 2 3 5 6 7 8 9 10 11 12 13 14 15 cigarettes smoked per day 4 23 25 38 17 8 4 26 11 19 14 35 29 23 longevity 80 78 60 53 85 84 73 79 81 75 68 72 58 92 65
1. What is the explanatory variable and what is the response variable? 2. Create a scatter plot. Be sure to include labels and units. 3. Does it appear that there is a linear relationship between the mean number of cigarettes smoked per day and longevity? Is there a positive association or negative association? 4. Compute the linear regression equation and the correlation coefficient. 5. Interpret the slope and y-intercept in the context of cigarette smoking and longevity. 6. Use your model to predict the longevity for a man who smokes an average of 11 cigarettes per day. 7. Is the value you calculated in #6 an over or underestimation?. What is the residual? 8. Circle the point on your scatterplot that has the largest residual. 9. Would it be appropriate to try to predict the age of death of a man who smokes an average of 75 cigarettes per day? Why or why not. Use appropriate vocabulary in your answer. 10. Conduct a hypothesis test to determine whether the number of cigarettes is a good predictor of longevity. Include all the relevant steps. 11. Compute the 95% confidence interval for beta, the slope of the true regression equation. Write a sentence interpreting your interval.
Transcribed Image Text:1. What is the explanatory variable and what is the response variable? 2. Create a scatter plot. Be sure to include labels and units. 3. Does it appear that there is a linear relationship between the mean number of cigarettes smoked per day and longevity? Is there a positive association or negative association? 4. Compute the linear regression equation and the correlation coefficient. 5. Interpret the slope and y-intercept in the context of cigarette smoking and longevity. 6. Use your model to predict the longevity for a man who smokes an average of 11 cigarettes per day. 7. Is the value you calculated in #6 an over or underestimation?. What is the residual? 8. Circle the point on your scatterplot that has the largest residual. 9. Would it be appropriate to try to predict the age of death of a man who smokes an average of 75 cigarettes per day? Why or why not. Use appropriate vocabulary in your answer. 10. Conduct a hypothesis test to determine whether the number of cigarettes is a good predictor of longevity. Include all the relevant steps. 11. Compute the 95% confidence interval for beta, the slope of the true regression equation. Write a sentence interpreting your interval.
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