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 asize of 41 inches. Is the result close to the actual weight of 263 pounds? Use a significance level of 0.05.Chest size (inches)Weight (pounds)40 5338 43 44 58227 360 153 206 234 414Click the icon to view the critical values of the Pearson correlation coefficient r.What is the regression equation?-x (Round to one decimal place as needed.)

Consider the following preliminary calculations:…

Answered: The data show the chest size and weight…

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 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 50 inches. Is the result close to the actual weight of 407 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. 49 51 53 61 57 45 368 382 420 481 457 287 What is the regression equation? ŷ=0 + x (Round to one decimal place as needed.)
It is assumed that the average horsepower for domestic cars is less than the average horsepower for foreign cars. Test whether horsepower for domestic cars is greater than that of foreign cars. What is your dependent variable? Horsepower Highway MPG Drivetrain Manufacturer
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.9 8.4 9.7 7.4 9.6 7.1 Weight (kg) 162 214 263 140 254 155 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = + x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2 cm is kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The…
Use the value of the linear correlation coefficient r to find the coefficient of determination and the percentage of the total variation that can be explained by the linear relationship between the two variables. r=0.853
2
Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 30 minutes. How does the result compare to the actual tip amount of $4.70? Use a significance level of 0.05. Distance 1.80 12.71 1.32 Time 1.65 8.51 1.40 1.02 2.47 Fare Tip 25.00 27.00 8.00 16.30 36.80 7.80 9.80 31.75 12.30 1.50 0.00 0.00 1.96 2.98 2.46 11.00 31.00 18.00 8.00 18.00 7.80 14.30 2.34 4.29 The regression equation is ŷ =+ (x. (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.)
Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed actress/actor ages in various years, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 30 years. Is the result within 5 years of the actual Best Actor winner, whose age was 37 years? Use a significance level of 0.05. Best Actress 29 30 28 60 31 35 46 30 62 23 46 51 D Best Actor 43 37 38 46 53 48 57 50 37 58 44 31 Find the equation of the regression line. + (Round the y-intercept to one decimal place as needed. Round the slope to three decimal places as needed.)
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 regression equation, lotting the diameler be the predictor (x) variable. Find the best predicted circumference of a marble with a diameter of 1.3 cm. How does the result compare to the achual circumference of 4.1 cm? Use a significance level of 0.05 Baseball Basketball Golf Soccer Tennis Ping Pong Volleyball 7.3 Diameter Circumference 23.5 4.4 21.9 7.1 3.9 21.4 22.9 73.8 13.8 68.8 22.3 12.3 67.2 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = x (Round to five decimal places as needed.)
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 50 inches. Is the result close to the actual weight of 417 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) FClick the icon to view the critical values of the Pearson correlation coefficient r. 49 51 53 61 57 45 D 368 382 420 481 457 287 What is the regression equation? y=+x (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r n 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 25 30 35 40 45 50 60 70 80 90 100 n Critical Values of the Pearson Correlation Coefficient r α=0.05 α = 0.01 0.950 0.878 0.811 0.754 0.707 0.666 0.632 0.602 0.576 0.553 0.532 0.514 0.497 0.482 0.468 0.456 0.444 0.396 0.361 0.335 0.312 0.294 0.279 0.254 0.236 0.220 0.207 0.196 α = 0.05 Print 0.990 0.959 0.917 0.875 0.834 0.798 0.765 0.735…
14
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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