An auto manufacturing company wanted to investigate how the price of one of its car models depreciateswith ages. The research department at the company took a sample of 8 cars of this model and collectedinformation on the ages (in years) and prices (in hundreds of dollars).6.AgePrice 188.3945021145423699Here is a scatterplot of the data.Age versus price of 8 cars1601401201008060402010AgeA.Is the relationship positive or negative?O positiveO negativePrice (in hundreds ofdollars) B. Use the 2 points (3,99) and (8,18) to calculate a line of fit.Enter the value of the slope, using the exact value.Enter the value of the y intercept, using the exact value.C. Which of the following is the equation of the regression line?Oy = -0.05x -+ 8.49Oy = -16.6x +150.4Oy = 150.4x-16.6correct answer is not givenD. Is it acceptable to use the regression equation to predict the value of a car that is 10 years old?O No, because the value is beyond the limits of the dataO No, because the value would be negative.O Yes, because you can always substitute numbers into an equation.O Yes, because the value of R is very stong.

Answered: Image /qna-images/answer/6af89d6d-e7da-4486-b6b8-7f6aefbc5fa5.jpg

Answered: B. Use the 2 points (3,99) and (8,18)…

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 and scatter plot show the number of years of experience, x, and the hourly pay rate, y, for each of 10 cashiers in Arizona. The equation of the line of best fit is y = 0.9xr+8.22. Experience, x Hourly pay rate, y (in years) 22 (in dollars) 20+ 0.8 7.47 18- 1.1 9.46 +16- Hourly 2.2 8.91 14+ pay rate 3.1 11.46 (in dollars) 124 10 3.8 12.31 3.8 13.00 6- 5.0 14.50 6.9 12.40 8.5 15.44 10 10 11 8.8 15.79 Experience (in years) Use the equation of the line of best fit to fill in the blanks below. Give exact answers, not rounded approximations. Observed hourly pay Predicted hourly pay Number of years Residual (in dollars) rate rate of experience (in dollars) (in dollars) 5.0 6.9
How do I go about these two questions followin this asnwer above: B) Ater removing the point with coordinates (2,9), use the pairs of values for the remaining 9 points and find the equation of the regression line. c. Compare the results from parts (a) and (b).
The point (9.1,71) is a point on the scatterplot. Draw the graph of the regression line on the scatterplot on the back of this paper using the y-intercept and the point(I = 3,y = 191). Indicate the residual for z = 9.1 by drawing the vertical line between(9.1, 71) and the corresponding point on the regression line. Will the residual be a positive or negative value?(f) Find the value of the residual for x = 9.1.
Find the equation of the line of best it from the data in the table. Round the slope and the y-intercept to the nearest hundredth. x0345 12 8269 12 O A y=0.53x+4.88 OB. y=0.63x+4.88 Oc. y=043x+4.98 O D. y=073x +4.98
The table and scatter plot show the time spent studying, x, and the midterm score, y, for each of 10 students. The equation of the line of best fit is y = 4.2x+11.58. Time spent Midterm 100- studying, x (in hours) score, y 90 3.3 21.51 4.0 28.04 Midterm score 60 Xx 4.8 22.89 50- 5.7 36.06 40- 6.9 36.28 30- 20- 8.2 58.20 104 8.5 45.42 14 10 12 14 16 18 20 22 24 12 9.1 56.72 12.1 70.00 Time spent studying (in hours) 15.0 74.58 Use the equation of the line of best fit to fill in the blanks below. Give exact answers, not rounded approximations. Time spent studying (in hours) Observed midterm Predicted midterm Residual Score Score 12.1 15.0
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
Using data from the attatched chart, and the equation stated below, answer questions a,b,and c. linear equation: y=0.078x+2.42 a. Write a linear equation in slope-intercept form that models the data. b. Use the equation from part (a) to project the demand for teachers in 2024. c. Project the year when the demand for teachers will reach 6 million.
The table shows the total square footage (in billions) of retailing space at shopping centers and their sales (in billions of dollars) for 10 years. The equation of the regression line is y = 516.388x - 1699.161. Complete parts a and b. Total Square Footage, X Sales, y 4.9 5.1 5.2 5.4 5.6 5.7 ********** (a) Find the coefficient of determination and interpret the result. 5.9 5.8 (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? 5.9 883.3 937.6 986.3 1059.5 1107.2 1200.9 1281.1 1340.8 1438.2 1536. 6.2 OA. The coefficient of determination is the fraction of the variation in sales that can be explained by the variation in total square footage. The remaining fraction of the variation is unexplained and is due to other factors or to sampling error. OB. The coefficient of determination is the fraction of the variation in sales that is unexplained and is due to other factors or sampling error. The remaining fraction of the variation is…
Consider the following regression equation: y = -3.9 + 7.2x What does 7.2 depict in the regression equation above? a) The direction of the line. b) The y-intercept. c) The point where x = 0. d) The y when x = 7.2.
The table shows the amounts of crude oil (in thousands of barrels per day) produced by a certain country and the amounts of crude oil (in thousands of barrels per day) imported by the same country for seven years. The equation of the regression line is y = - 1.269x + 16,635.60. Complete parts (a) and (b) below. Produced, x Imported, y 5,718 5,612 5,409 5,228 5,128 5,006 O 9,106 9,631 10,000 10,140 10,133 10,059 5,759 9,318 (a) Find the coefficient of determination and interpret the result. (Round to three decimal places as needed.)
Using the weights (Ib) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58 9-0.00749x, where x represents weight Complete parts (a) through (d). Click the icon to view the car data. 東 b. What are the specific values of the slope and y-intercept of the regression line? O A. The slope is 58.9 and the y-intercept is 0.007499. B. The slope is -0.00749 and the y-intercept is 58.9. O C. The slope is 58.9 and the y-intercept is -0.00749. O D. The slope is 0.00749 and the y-intercept is 58.9. c. What is the predictor variable? O A. The predictor variable is highway fuel consumption, which is represented by x. B. The predictor variable is weight, which is represented by x. O C. The predictor variable is weight, which is represented by y O D. The predictor variable is highway fuel consumption, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel…
a and b please

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