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 theregression 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.Height, xStories, y762516214651545508424913948036(a) x = 502 feet(c) x = 315 feetFind the regression equation.ŷ=x+(Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)(b) x = 645 feet(d) x = 731 feet

Given that the following tableHeight (x)Stories (y)7625162146515455084249139480236

Answered: ind the equation of the regression line…

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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00 B T F. E 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. (a) x = 502 feet (c) x = 810 feet (b) x = 645 feet (d) x = 731 feet 520 508 494 484 Height, x Stories, y 620 992 51 %3D 45 42 Find the regression equation. ミイ + X (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Enter your answer in the edit fields and then click Check Answer. Check Answer parts 5. remaining Examplify Clear All Type here to search 直。 +66 (口 F5 PrtScn (口 F1 F2 F3 F4 & #3 V 2$ 2 5. 9. 7. 4. R H. N
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $3 million. Is the result close to the actual number of viewers, 7.0 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) 99 7 4 9 15 4.1 2.2 1.4 4.4 8.3 8.8 4.9 4.3 = Click the icon to view the critical values of the Pearson correlation coefficient r. Critical Values of the Pearson Correlation Coefficient r What is the regression equation? y=+ x (Round to three decimal places as needed.) Critical Values of the Pearson Correlation Coefficient r a =0.05 0.950 What is the best predicted number of viewers for a television star with a salary of $3 million? a= 0.01 NOTE: To test H p=0 0.990 against H, p#0, reject H, if the absolute value of r is greater than the critical value in the table. 14 The best predicted number 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 table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y778 51621 47519 45510 42494 39473 38 (a) x=498 feet (b) x=647 feet (c) x=321 feet (d) x=727 fee Find the regression equation.
Find the explanation variation for the paired data. The equation of the regression line for the paired data below is y = 3x. Find the explained variation.
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 4 5 5 Test score, y 39 45 50 49 63 73 (a) x=2hours (b) x=2.5hours (c) x=14hours (d) x=3.5hours 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. (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. Height, x Stories, y 775 619 519 508 (a) x= 501 feet (c) x=318 feet 491 474 (b) x = 651 feet (d) x= 729 feet 53 47 45 43 38 37 Find the regression equation. (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. (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. Height, x 774 625 521 508 497 477 (a) x=499 feet (b) x=644 feet Stories, y 51 47 45 42 37 35 (c) x=318 feet (d) x=732 feet
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. Height, x Stories, y Un ne conteui yrapii veIUW. O A. Stories A 60- Height (feet) OO ( 800 (a) Predict the value of y for x = 503. Choose the correct answer below. A. 40 OB. 47 O C. 50 O D. not meaningful (b) Predict the value of y for x = 652. Choose the correct answer below. 774 51 OA. 54 O B. 47 OC. 40 O D. not meaningful (c) Predict the value of y for x = 810. Choose the correct answer below. OA. 54 625 47 OA. 54 OB. 50 OC. 47 O D. not meaningful (d) Predict the value of y for x = 725. Choose the correct answer below. B. 40 ΕΛ 521 45 O B. Stories 60+ 508 41 0 Height (feet) 800 497 38 477…
17. A company is analyzing the makeup of its employees based on gender and education level. What is the expected value in the shaded cell of the two-way contingency table?
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 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place 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. (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 0 Test score, y 38 Find the regression equation. 2 3 3 4 6 45 50 49 61 68 0 (a) x = 2 hours (c) x = 15 hours (b)x=4.5 hours (d) x=2.5 hours y = X + (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…

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