Use the given set of bivariate data. (2, 7), (4, 3), (3, 5). Calculate the covariance sxv: Sxy' Sxy %3D Calculate the correlation coefficient r. Calculate the equation.of the regression line. ý = Plot the points and the line on a scatterplot. 24 22 20 18 16 14 12 40 No Solution Help 2. 9.

Use the given set of bivariate data. (2, 7), (4, 3), (3, 5). Calculate the covariance sxv: Sxy' Sxy %3D Calculate the correlation coefficient r. Calculate the equation.of the regression line. ý = Plot the points and the line on a scatterplot. 24 22 20 18 16 14 12 40 No Solution Help 2. 9.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

d the linear regression equation and the value of the linear correlation coefficient r. Round to 2 decimals. 19) The paired data below consist of the costs of advertising (in thousands of dollars) and the number of products sold (in thousands): Cost| 7 2 3 4 2 5 9 10 Number 85 52 55 68 67 80 83 73 to Cost r =
Q3: From the following data: 2 5 y 5 4 6 3 1) Draw scatter plot. 2) Determine the regression line y = a + b x. 3) Predict the value of y for the value of x = 4.6. 4) Calculate the correlation coefficient. 4 8 3 5
The ages (in years) of five men and their systolic pressures. (You can use technology) Age, x 16 25 39 45 49 Systolic blood pressure, y 109 122 143 132 199 a. calculate the correlation coefficient, r b. calculate the critical r c. Is there a significant linear relationship between x and y? d. find the equation of regression line e Interpret the slope and the y-intercept f. Predict the systolic blood pressure for a man who is 40 years old.
Solved question 31
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 41 inches. Is the result close to the actual weight of 153 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. 40 53 38 43 44 58 D 227 360 153 206 234 414 What is the regression equation? y=+x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 41 inches? The best predicted weight for a bear with a chest size of 41 inches is (Round to one decimal place as needed.) Is the result close to the actual weight of 153 pounds? O A. This result is exactly the same as the actual weight of the bear. O B. This result is close to the actual weight of the bear. O C. This result is very close to the actual weight of the bear. O D. This…
Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 7 recent years are shown. Number of fires x 58 47 84 62 57 72 69 Number of acres burned y 54 43 80 58 53 68 65 The correlation coefficient for the data is =r1 and =α0.05 . Should regression analysis be done? The regression analysis should not be done. The regression analysis should be done. Find the equation of the regression line. =y′+abx =a =b Find y′ when =x50 . =y′
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 number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x 1 2 2 3 4 6 Test score, y 39 44 52 47 64 70 (a)x=2hours (b)x=3.5hours (c)x=13hours (d)x=1.5hours Find the regression equation. y=______ x+ (_____) (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)
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. Calories, x Sodium, y (a) x= 150 calories (c) x= 120 calories (b) x= 90 calories (d) x=60 calories 160 180 130 130 80 190 415 465 350 380 270 540 (c) Predict the value of y for x= 120. Choose the correct answer below O A. 347 151 O B. 279.801 O C. 414.501 O D. not meaningful (d) Predict the value of y for x= 60. Choose the correct answer below. O A. 414.501 O B. 212.451 tes O C. 279.801
The following table shows students’ number of absences, x, and the student’s final grade,, y.# of absences x 6 2 15 9 12 5 8 Final grade y 82 86 43 74 58 90 78a) Calculate r, the correlation coefficient ________________b) Find the equation of the regression line __________________________________c) If a student is absent 4 times,, what grade does your regression line predict?__________________
Data Set: {(-3, 4), (-2, 3), (–1,3), (0, 7), (1, 5), (2, 6), (3, 1)} 1. The regression line is: y = when the 2. Based on the regression line, we would expect the value of response variable to be explanatory variable is 0. 3. For each increase of 1 in of the explanatory variable, we can expect a(n) in the response variable. 4. If x = -3.5, the y = This is an example of 5. The correlation coefficient is r = (Round to the nearest hundredth.) Check of
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 number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x Test score, y 4 47 (a) x=3 hours (c) x= 13 hours (b) x= 2.5 hours (d) x= 3.5 hours 5 6 40 45 52 63 72 Find the regression equation. y=x+ (O (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)
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