When the heights (in inches) and shoe lengths (also in inches) were measured for a large random sample ofindividuals, it was found that r = 0.89, and a regression equation was constructed in order to further explorethe relationship between shoe length and height, with height being the response variable. From thisinformation, what can we conclude?The correlation coefficient should have no units.The regression equation relating shoe length to height must have a slope equal to 0.89.The regression equation relating shoe length to height must have a positive intercept.O Approximately 89% of the variability in height can be explained by the regression equation.Because the value of r is less than 1, we should characterize this relationship as being weak.

Answered: Image /qna-images/answer/9ac8a5a4-19bc-4c86-a1ad-fe2a25ef02eb.jpg

Answered: When the heights (in inches) and shoe…

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
on the basis of the value of linear correlation coefficient, would you conclude, at the /r/>0.9 level, that the data can be reasonably modeled linear equation?
The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 14.5 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 14.5 cm, what is the single value that is the best-predicted height for that person? The regression equation is Height=64.8+5.70 Foot Length Predictor Coef SE Coef T P Constant 64.79 11.98 5.41 0.000 Foot Length 5.7004 0.4126 13.82 0.000 S=5.50488 R-Sq=70.6% R-Sq(adj)=69.8% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 147.446 1.796 (143.130, 151.762) (135.900, 158.992) Values of Predictors for New Observations New Obs Foot Length 1 14.5…
The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 21.4 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 21.4 cm, what is the single value that is the best-predicted height for that person? The regression equation is Height=52.3+4.58 Foot Length Predictor Coef SE Coef T P Constant 52.33 11.01 4.75 0.000 Foot Length 4.5818 0.4872 9.40 0.000 S=5.50437 R-Sq=70.8% R-Sq(adj)=70.0% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 150.381 1.722 (146.730, 154.032) (138.758, 162.004) Values of Predictors for New Observations New Obs Foot Length 1 21.4 The…
The fish in my pond have mean length 12 inches and mean weight 5 pounds. The correlation coefficient of length and weight is .8. If the length of a particular randomly selected fish is reported to be 12 inches, then what should we predict for the weight of that fish using simple linear regression?
In a simple linear regression model with one predictor variable, what is the coefficient of determination (R-squared) if the Pearson's correlation coefficient between the predictor and response variable is 0.6?
The fish in my pond have mean lenth 14 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .8 pounds. The correlation coefficient of length and weight is .4. If the length of a particular randomly selected fish is reported to be 15 inches, then what should we predict for the weight of that fish using simple linear regression?
The average midterm score in a large statistics class was 65 with an SD of 15. The average final score in the same class was 70 with an SD of 10. The correlation coefficient between midterm and final scores was r-0.6. Using the regression line, we predict the final score of a student with a midterm score of 80 to be but this prediction is likely to be off by about
If there is no significant correlation between the response and explanatory variables, would the slope of the regression line be (a) positive (b) negative (c) zero?
The fish in my pond have mean length 14 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .8 pounds. The correlation coefficient of length and weight is .7. If the length of a particular randomly selected fish is reported to be 17 inches, then what should we predict for the weight of that fish using simple linear regression?

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