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\({s_e}\)Notation Using Data Set 1 鈥淏ody Data鈥 in Appendix B, if we let the predictor variable x represent heights of males and let the response variable y represent weights of males, the sample of 153 heights and weights results in\({s_e}\)= 16.27555 cm. In your own words, describe what that value of \({s_e}\)represents.

Short Answer

Expert verified

The value of \({s_e}\) equal to 16.27555 cm explains that the average distance of the observed value of weights of males from the fitted values is obtained using the regression equation.

Step by step solution

01

Given information

A regression equation between the response variable 鈥渨eights of male鈥 and the predictor variable 鈥渉eights of males鈥 is given.

The value of \({s_e}\) is 16.27555 cm.

02

Meaning of \({s_e}\)

\({s_e}\)stands for the standard error of the estimate.

It describes the mean distance between the observed values of the response variable and the predicted values.

Here, the response variable is the weights of males, and the standard error of the estimate\(\left( {{s_e}} \right)\)is 16.27555 cm.

Thus, the standard error of estimate tells that the average difference between the measured weights and the weights predicted by the regression equation equals 16.27555 cm.

It represents how much the sample points deviate from the regression line.

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Most popular questions from this chapter

The following exercises are based on the following sample data consisting of numbers of enrolled students (in thousands) and numbers of burglaries for randomly selected large colleges in a recent year (based on data from the New York Times).

Enrollment (thousands)

53

28

27

36

42

Burglaries

86

57

32

131

157

True or false: If the sample data lead us to the conclusion that there is sufficient evidence to support the claim of a linear correlation between enrollment and number of burglaries, then we could also conclude that higher enrollments cause increases in numbers of burglaries.

In Exercises 9 and 10, use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line.

Explore! Exercises 9 and 10 provide two data sets from 鈥淕raphs in Statistical Analysis,鈥 by F. J. Anscombe, the American Statistician, Vol. 27. For each exercise,

a. Construct a scatterplot.

b. Find the value of the linear correlation coefficient r, then determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables.

c. Identify the feature of the data that would be missed if part (b) was completed without constructing the scatterplot.

x

10

8

13

9

11

14

6

4

12

7

5

y

9.14

8.14

8.74

8.77

9.26

8.10

6.13

3.10

9.13

7.26

4.74

Interpreting a Computer Display. In Exercises 9鈥12, refer to the display obtained by using the paired data consisting of Florida registered boats (tens of thousands) and numbers of manatee deaths from encounters with boats in Florida for different recent years (from Data Set 10 in Appendix B). Along with the paired boat, manatee sample data, Stat Crunch was also given the value of 85 (tens of thousands) boats to be used for predicting manatee fatalities.


Testing for Correlation Use the information provided in the display to determine the value of the linear correlation coefficient. Is there sufficient evidence to support a claim of a linear correlation between numbers of registered boats and numbers of manatee deaths from encounters with boats?

Coefficient of Determination Using the heights and weights described in Exercise 1, the linear correlation coefficient r is 0.394. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide?

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