**Provide an appropriate response.**The data below are the average one-way commute times (in minutes) for selected students and the number of absences for those students during the term. Find the equation of the regression line for the given data. What would be the predicted number of absences if the commute time was 95 minutes? Is this a reasonable question? Round the predicted number of absences to the nearest whole number. Round the regression line values to the nearest hundredth.| Commute time (min), x | 72 | 85 | 91 | 90 | 88 | 98 | 75 | 100 | 80 | 85 ||-----------------------|----|----|----|----|----|----|----|-----|----|----|| Number of absences, y | 3 | 7 | 10 | 10 | 8 | 15 | 4 | 15 | 5 | 5 |A) \( \hat{y} = 0.45x - 30.27; 12 \) absences; No, it is not reasonable. 95 minutes is well outside the scope of the model.B) \( \hat{y} = 0.45x - 30.27; 12 \) absences; Yes, it is reasonable.C) \( \hat{y} = 0.45x + 30.27; 73 \) absences; Yes, it is reasonable.D) \( \hat{y} = 0.45x + 30.27; 73 \) absences; No, it is not reasonable. 95 minutes is well outside the scope of the model.

The independent variable is Commute time. The dependent variable is Number of absences. We have to…

Answered: The data below are the average one-way…

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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You generate a scatter plot using Excel. You then have Excel plot the trend line and report the equation and the r2r2 value. The regression equation is reported asy=60.71x+33.51y=60.71x+33.51and the r2=0.1764r2=0.1764.What is the correlation coefficient for this data set?r =
Last two subpart please
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
The scatter plot shows the number of hours worked and money spent on entertainment by each of 24 students. Also shown is the line of best fit for the data. Fill in the blanks below. 70 65 60 55. Amount of 50 × 45 money spent on entertainment 40+ (in dollars) 35. × × 30 25 20 15. × 10+ 5-x 0 6 8 10 12 14 16 18 20 22 Number of hours worked (a) For these 24 students, as the number of hours increases, the amount of money spent tends to (Choose one) (b) For these 24 students, there is (Choose one) money spent. correlation between the number of hours worked and the amount of (c) Using the line of best fit, we would predict that a student working 8 hours would spend approximately (Choose one) ▼ ك ?
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. 170 480 Calories, x Sodium, y 560 Find the regression equation. ŷ=x+ (Round to three decimal places as needed.) Choose the correct graph below. OA. 0ff 0 200 150 430 Calories 130 320 a 130 380 O B. 560 0 80 250 200 G Calories (a) Predict the value of y for x = 160. Choose the correct answer below. OA. 390.863 OB. 440.983 OC. 591.343 O D. not meaningful (b) Predict the value of y for x = 90. Choose the correct answer below. 190 510 (a) x (c) x 160 calories 140 calories O C. A 560+ 0-T 0 200 Calories (b) x = 90 calories (d) x = 220 calories OD. 560- 0 200 Calories Q
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. 483 Height, x Stories, y 772 628 518 508 51 48 45 42 496 37 (a) x=499 feet (c) x=315 feet (b)x=639 feet (d) x = 732 feet 35 Find the regression equation. ŷ=x+ (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. O C. OB. O D. OA. Q Q ↓ 0 0 Height (feet) Height (feet) (a) Predict the value of y for x = 499. Choose the correct answer below. OA. 51 OB. 40 60+ 0- 800 60+ 0- 800 Q A 60- → 0 Height (feet) 800 60- 0- 800 0 Height (feet)
PLEASE ANSWER 4,5 AND 6

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