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let the predictor variable x be the first variable given. Use the given data to find the regression equation and the best predicted value of the response variable. Be sure to follow the prediction procedure summarized in Figure 10-5 on page 493. Use a 0.05 significance level.

Heights (cm) and weights (kg) are measured for 100 randomly selected

adult males (from Data Set 1 “Body Data” in Appendix B). The 100 paired measurements yield\(\bar x = 173.79\)cm,\(\bar y = 85.93\)kg, r= 0.418, P-value = 0.000, and\(\hat y = - 106 + 1.10x\). Find the best predicted value of\(\hat y\)(weight) given an adult male who is 180 cm tall.

Short Answer

Expert verified

The predicted value of the \(\hat y\)(weight) for an adult male who is 180 cm tall is 92.0 kg.

Step by step solution

01

Given information

The sample number of adult males is\(n = 100\). x represents theheights of adult males and y represents the weights of adult males.

The mean height and weight are \(\bar x = 173.79\)cm and \(\bar y = 85.93\) kg. The correlation coefficient is \(r = 0.418\) and the P-value is 0.000. The regression equation is \(\hat y = - 106 + 1.10x\).

02

Analyze the model

The statistical hypotheses are formed as,

\({H_0}:\) The correlation coefficient is not significant.

\({H_1}:\)The correlation coefficient is significant.

Since the P-value (0.000) is less than the level of significance (0.05). In this case, the null hypothesis is rejected.

Therefore, the correlation coefficient is significant.

Referring to figure 10-5, the regression model is a good model.

The regression equation can be used to predict the value of y.

03

Compute the predicted value

Thepredicted valueis computed as,

\(\begin{array}{c}\hat y = - 106 + \left( {1.10 \times 180} \right)\\ = - 106 + 198\\ = 92.0\end{array}\).

Thus, the predicted value of the \(\hat y\)(weight) for an adult male who is 180 cm tall is 92.0 kg.

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Most popular questions from this chapter

Best Multiple Regression Equation For the regression equation given in Exercise 1, the P-value is 0.000 and the adjusted \({R^2}\)value is 0.925. If we were to include an additional predictor variable of neck size (in.), the P-value becomes 0.000 and the adjusted\({R^2}\)becomes 0.933. Given that the adjusted \({R^2}\)value of 0.933 is larger than 0.925, is it better to use the regression equation with the three predictor variables of length, chest size, and neck size? Explain.

Testing for a Linear Correlation. In Exercises 13–28, construct a scatterplot, and find the value of the linear correlation coefficient r. Also find the P-value or the critical values of r from Table A-6. Use a significance level of A = 0.05. Determine whether there is sufficient evidence to support a claim of a linear correlation between the two variables. (Save your work because the same data sets will be used in Section 10-2 exercises.)

CSI Statistics Police sometimes measure shoe prints at crime scenes so that they can learn something about criminals. Listed below are shoe print lengths, foot lengths, and heights of males (from Data Set 2 “Foot and Height” in Appendix B). Is there sufficient evidence to conclude that there is a linear correlation between shoe print lengths and heights of males? Based on these results, does it appear that police can use a shoe print length to estimate the height of a male?

Shoe print(cm)

29.7

29.7

31.4

31.8

27.6

Foot length(cm)

25.7

25.4

27.9

26.7

25.1

Height (cm)

175.3

177.8

185.4

175.3

172.7

What is the relationship between the linear correlation coefficient rand the slope\({b_1}\)of a regression line?

let the predictor variable x be the first variable given. Use the given data to find the regression equation and the best predicted value of the response variable. Be sure to follow the prediction procedure summarized in Figure 10-5 on page 493. Use a 0.05 significance level.

For 50 randomly selected speed dates, attractiveness ratings by males of their

female date partners (x) are recorded along with the attractiveness ratings by females of their male date partners (y); the ratings are from Data Set 18 “Speed Dating” in Appendix B. The 50 paired ratings yield\(\bar x = 6.5\),\(\bar y = 5.9\), r= -0.277, P-value = 0.051, and\(\hat y = 8.18 - 0.345x\). Find the best predicted value of\(\hat y\)(attractiveness rating by female of male) for a date in which the attractiveness rating by the male of the female is x= 8.

In Exercises 5–8, we want to consider the correlation between heights of fathers and mothers and the heights of their sons. Refer to the

StatCrunch display and answer the given questions or identify the indicated items.

The display is based on Data Set 5 “Family Heights” in Appendix B.

A son will be born to a father who is 70 in. tall and a mother who is 60 in. tall. Use the multiple regression equation to predict the height of the son. Is the result likely to be a good predicted value? Why or why not?

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