The least-square regression line for the given data is \( \hat{y} = 0.449x - 30.27 \). Determine the residual of a data point for which \( x = 90 \) and \( y = 10 \), rounding to three decimal places.| Temperature, \( x \) | 72 | 85 | 91 | 90 | 88 | 98 | 75 | 100 | 80 ||----------------------|----|----|----|----|----|----|----|----|----|| Number of absences, \( y \) | 3 | 7 | 10 | 10 | 8 | 15 | 4 | 15 | 5 |Options:- A. \(-0.14\)- B. \(20.14\)- C. \(115.78\)- D. \(10.14\) The least-square regression line for the given data is \(\hat{y} = 2.097x - 0.552\). Determine the residual of a data point for which \(x = 0\) and \(y = -1\), rounding to three decimal places.Values:\[\begin{array}{c|cccccccccc}x & -5 & -3 & 4 & 1 & -1 & -2 & 0 & 2 & 3 & -4 \\\hliney & -10 & -8 & 9 & 1 & -2 & -6 & -1 & 3 & 6 & -8 \\\end{array}\]Options:- A. \(-0.552\)- B. \(-0.448\)- C. \(2.649\)- D. \(-1.552\)

The given least-square regression line is y^=0.449x-30.27.

Answered: The least-square regression line for…

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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Which of the following is not one of the uses of a scatter plot and regression line a. to estimate the average y at a specific value of x. b. All three are uses of the scatterplot and regression line c. to determine if a change in x causes a change in y d. to predict y at a specific value of x.
Which of the following is the most appropriate equation to model the data? ŷ = –10.89x + 0.85 ŷ = –0.85x + 10.89 ŷ = 10.89(0.85)x ŷ = 0.85(10.89)x
Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-intercept to at least four decimal places. 43 37 15 19 24 37 18 y 72 67 25 29 42 65 32 Send data to Excel Regression line equation: y = 1.7777x + 1.5838
37-38. Given the following data pairs (x, y): (1, 1.24), (2, 5.23). (3, 7.24). (4, 7.60), (5, 9.97), (6, 14.31), (7, 13.99).(8, 14.88), (9, 18.04), (10, 20.70). Find the regression equation O A. y = 0.490 x - 0.053 O B. y = 2.04 x O C. y = 1.98 x + 0.436 O D. y = 0.49 x
The money raised and spent (both in millions of dollars) by all congressional campaigns for 8 recent 2-year periods are shown in the table. The equation of the regression line is y = 0.942x +27.609. Find the standard error of estimate s, and interpret the result. 793.9 1042.3 957.7 1203.3 450.7 673.7 745.1 778.6 Money raised, x Money spent, y 734.8 1024.2 929.1 1160.6 448.6 697.9 735.7 751.2 Find the standard error of estimate s, and interpret the result. (Round to three decimal places as needed.) How can the standard error of estimate be interpreted? O A. The standard error of estimate of the money raised for a specific amount of money spent is about s, million dollars. O B. The standard error of estimate of the money spent for a specific amount of money raised is about s, million dollars.
An 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.
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Use the regression equation to predict the value of y for x = -1.6. Assume that the variables x and y have a significant correlation. -5-34 1 -1-2 X 0 2 3 -4 y - -10 8 9 1 -2-6 -1 3 6 -8 OA. 1.214 OB. -2.803 O C. 2.980 OD. -3.907

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The least-square regression line for the given data is y = 0.449x-30.27. Determine the residual of a data point for which x= 90 and y=10, rounding to three decimal places. Temperature, x Number of absences, y OA. -0.14 B. 20.14 C. 115.78 OD. 10.14 72 3 85 7 91 10 90 10 88 8 98 75 100 15 4 15 80 5