MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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- Consider the following data set, where y is the final grade in a math class and is the average number of hours the student spent working on math each week. hours/week X 6.5 10 11 12.5 13 14.5 15 17.5 19.5 20 Grade y 66.1 63 71.4 72.5 79.2 94.3 95 91.5 100 98 The regression equation is y = 3x + 41.3. a) Explain what the value of the slope means in this situation, where y is the final grade in a math class and is the average number of hours the student spent working on math each week. O For each additional hour per week that a student studies on average, their final grade will be about 3 points. O For each additional hour per week that a student studies on average, their final grade will be about 41.3 points higher. For each additional hour per week that a student studies on average, their final grade will be about 3 points higher. O For each additional hour per week that a student studies on average, their final grade will be about 41.3 points. b) Explain what the value of the…arrow_forwardconsider the following points (1.3,2.4) (2.5,2.4) (3.3,2) (4.2,1.8) (5.1,1.6) find the equation of the regression line (round the values 2 decimal placed.arrow_forwardPlease helparrow_forward
- Run a regression analysis on the following data set, where y is the final grade in a math class and x is the average number of hours the student spent working on math each week. hours/week Grade y 8. 72.2 8. 58.2 10 60 11 71.4 14 92.6 14 76.6 14 75.6 14 83.6 15 81 20 100 State the regression equation with constants accurate to 2 decimal places. ý = What is the predicted value for the final grade when a student spends an average of 15 hours each week on math? Round to 2 decimal places. Question Help: D Video 1 Video 2 Submit Questionarrow_forwardPerform a linear regression analysis on the following data and determine the "a" coefficient (i.e., slope): Y 4.99 22.19 1.96 9.89 2.98 11 9 40.46 4.04 18.93 6.06 25 0.88 0.19 8.02 34.02 6.97 28.03arrow_forwardAn automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.arrow_forward
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