Elementary Statistics (13th Edition)
13th Edition
ISBN: 9780134462455
Author: Mario F. Triola
Publisher: PEARSON
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Chapter 10.2, Problem 26BSC
Regression and Predictions. Exercises 13-28 use the same data sets as Exercises 13-28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.
26. POTUS Using the president/opponent heights, find the best predicted height of an opponent of a president who is 190 cm tall. Does it appear that heights of opponents can be predicted from the heights of the presidents?
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Arm circumferences (cm) and heights (cm) are measured from randomly selected adult females. The 139 pairs of measurements yield x = 31.99 cm, y = 163.33 cm,
r= 0.032, P-value = 0.708, and y = 158 + 0.1703x. Find the best predicted value of y (height) given an adult female with an arm circumference of 35.0 cm. Let the
predictor variable x be arm circumference and the response variable y be height. Use a 0.05 significance level.
%3D
.....
The best predicted value is
cm.
(Round to two decimal places as needed.)
1) Draw a scatter plot. (Do not need)2) Calculate the r statistic. (Do not need) 3) Find the regression equation. (Do not need) 4) Use the regression equation to calculate two predicted y values from two x values.5) Use the points from step 4 to draw a regression line on the scatter plot from step 1, labeling the line with the regression equation.
x= 7, 9, 6, 12, 9, 5
Y= 6, 6, 3, 5, 6, 4
Heights (in centimeters) and weights (in kilograms) of 77 supermodels are given below. Find the regression equation, letting the first variable be the independent (?) variable, and predict the weight of a supermodel who is 169 cm tall.
Height: 174, 176, 176, 178, 168, 166, 172
Weight: 55, 56, 55, 58, 50, 47, 53
The regression equation is ?̂= + ?. The best predicted weight of a supermodel who is 169 cm tall is .
Chapter 10 Solutions
Elementary Statistics (13th Edition)
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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?arrow_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardIf your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?arrow_forward
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