BEEBThe scatterplot below shows the relationship between miles driven, x, and sale price, y, for several used cars of thesame model on a used car website. Which of the values is the best estimate of the correlation coefficient for thisdata set?NOV1Price ($)Price versus Miles Driven for Used Cars30,00020,00010,000O-0.900Mark this and return50,000 100,000 150,000 200,000Miles Driven (Miles)Save and ExittvNext♫AAA01:04:04SubmitOAa Price ($)20,00010,000-0.90-0.500.100.500.900Mark this and return50,000 100,000 150,000 200,000Miles Driven (Miles)Save and ExitNext

Properties of correlation: The sign of the correlation coefficient indicates the direction of the…

Answered: The scatterplot below shows the…

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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Use the table to find the following, unit price being your (x) and units sold (y). a. Find linear correlation coefficient. Years of Experience Salary in 1000$ 2 3. 15 28 42 13 64 50 16 90 11 58 8 54 b. Find the line of best fit. c. How many units will sell if the unit price was 0.85.
An economist wants to determine whether there is a linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. The data are shown in the table below. c. Compute and interpret the correlation coefficient. d. Compute and interpret the coefficient of determination. e. Test for the significance of the linear relationship. Use a 0.05 level of significance. State your conclusion. Hint: Your conclusion is either of the following. • There is a significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. • There is no significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. GDP 1.6 3.6 4.9 1.1 0.9 2.9 2.7 2.3 1.6 1.5 (trillion dollars) Carbon Dioxide Emissions 428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5 (millions of metric tons)
3. The speed (in miles per hour) of 8 vehicles and their braking distance (in feet) when stopping on a dry surface speed, x braking distance, y 20 30 40 50 50 60 65 70 55 72 75 110 138 162 193 225 type of correlation: Conclusion
The data below are the ages and annual pharmacy b ills (in dollars) of 9 randomly selected employees. Calculate the linear correlation coefficient.Age, x: 30, 33, 37, 40, 43, 45, 49, 53, 57Pharmacy bill ($), y: 119, 123, 126, 134, 145, 148, 151, 153, 155A. 0.890B. 0.908C. 0.998D. 0.960
A manager wishes to determine the relationship between the number of years the manager's sales representatives have been with the company and their average monthly sales (in thousands of dollars). Calculate the linear correlation coefficient.Years with company, x: 6,7,14,11,12,19,7,5,15Sales, y: 21,23,68,52,55,51,38,45,110
Write a report using Word. Cover the following topics. This file contains some information about different cars. Car Weight (pounds), x Miles per Gallon, y A 2750 31.5 B 3120 27.9 C 3385 23.7 D 3740 23.3 E 4225 21 Create a scatterplot for the data in the Weight and Braking columns. Paste it here. a) Using StatCrunch or TI 83/84 to calculate the linear correlation between the data in the Weight and MPG columns. b) Explain the mathematical relationship between Weight and MPG based on the linear correlation coefficient. Be certain to include comments about the magnitude and the direction of the correlation. c) Write the equation for the least-squares regression line if there is one. d) Predict the miles per gallon of car C and compute the residual.
Stan measured the circumference of his classmates' heads and compared it with their current grade in Math. The results are in the given table. Head (cm) Grade 55 23 53 92 57 84 56 88 55 97 53 87 54 72 57 64 57 91 Is there a strong correlation in this data set? How do you know? Yes, there is a strong correlation because the slope is 126.5. No, there is NOT a strong correlation because the correlation coefficient is -0.89, which is close to -1.
r = - 1
The numbers of pass attempts and passing yards for seven professional quarterback for a recent year are listed in the table below. Round all answers to the nearest 1000th. Pass attempts (x) 449 565 528 197 670 351 218 Passing Yards (y) 3265 4018 3669 1141 5177 2362 1737 Calculate the sample correlation coefficient, r. Describe the type of correlation coefficient and interpret the correlation in the context of the data.

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