Given the following data set, let x be the explanatory variable and y be the response variable. x75 1 8812 y36102398 (a) If a least squares line was fitted to this data, what percentage of the variation in the y would be explained by the regression line? (Enter your answer as a percent.) ANSWER: % (b) Compute the correlation coefficient: r =
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Q: Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting…
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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 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click 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.)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 57 inches. Is the result close to the actual weight of 476 pounds? Use a significance level of 0.05. 44 Chest size (inches) Weight (pounds) 58 48 51 58 60 425 266 347 453 282 408 Click the icon to view the critical values of the Pearson correlation coefficient r. ..... What is the regression equation? y=+x (Round to one decimal place as needed.) Activate Windows View an example Get more help- Help me solve this O Type here to search hp delete insert prt sc f12 f1o fg 1 f7 f6 f5 f3 米 IOI f1 esc hom backspace 6. L. 4 U E R tab F G J. A caps lock pause 00 9, %24 3. %23A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a. For each 1-cm increase in height, weight tends to increase by about 0.32 kg b. There is no association between weight and height c. Height accounts for about 32% of the total variability in weight d. The correlation between weight and height is about 0.32
- Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 253.2 258.7 278.7 257.8 274.2 262.2 Height 1784.8 1771.0 1675.6 1645.9 1858.7 1710.1 1789.2 1737.4 the regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is enter your response here mm. (Round to the nearest integer as needed.)The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.
- 5. For the following set of data: Y 1 10 5 4 4 13 a. Find the regression equation for predicting Y from X. b. Does the regression equation account for a significant portion of the variance in the Y scores? Use a = .05 to evaluate the F-ratio %3D X27 33Select the most appropriate response. If the correlation between a person’s age and annual income is 0.60, then the coefficient of determination tells us that: 36% of the variation in a person’s annual income can be explained by the predictor variable age. 36% of a person’s annual income can be explained by their age 60% of the variation in a person’s annual income can be explained by the predictor variable age 60% of a person’s annual income can be explained by their ageThe 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 $6 million. Is the result close to the actual number of viewers, 8.9 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. 98 3.5 3 7 13 12 13 10 2 6.8 6.3 10.2 8.5 4.4 1.8 2.7 What is the regression equation? y=+x (Round to three decimal places as needed.) What is the best predicted number of viewers for a television star with a salary of $6 million? The best predicted number of viewers for a television star with a salary of $6 million is million. (Round to one decimal place as needed.) Is the result close to the actual number of viewers, 8.9 million? O A. The result is very close to the actual number of viewers of 8.9 million. O B. The…
