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In Exercises 5–8, we want to consider the correlation between heights of fathers and mothers and the heights of their sons. Refer to theStatCrunch display and answer the given questions or identify the indicated items.

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

Identify the multiple regression equation that expresses the height of a son in terms of the height of his father and mother.

Short Answer

Expert verified

The multiple regression equation is

\({\rm{Son}} = 18 + 0.504\;{\rm{Father}} + 0.277\;{\rm{Mother}}\).

Step by step solution

01

Given information

The analysis of the variance table for the multiple regression model is provided.

02

State the general equation of multiple regression

The multiple regression equation is

\(\hat y = {b_0} + {b_1}{x_1} + {b_2}{x_2} + ... + {b_n}{x_n}\), where \(\hat y\) is the predicted response variable and \({x_1},{x_2},...,{x_n}\)are the independent variables along with respective measure of coefficients \({b_0},{b_1},...,{b_n}\).

03

Compute the equation of multiple regression

Define the variables as follows:

Father: Height of father

Mother: Height of mother

Son: Height of son

From the output, the estimates of the variables are

\(\begin{array}{l}{b_0} = 17.96657,\\{b_1} = 0.50354896,\\{b_2} = 0.27714316.\end{array}\)

The multiple regression equation for the provided scenario is represented below:

\(\begin{array}{c}Son = {b_0} + {b_1}\;{\rm{Father}} + {b_2}\;{\rm{Mother}}\\ = 17.9666 + 0.504\;{\rm{Father}} + 0.277\;{\rm{Mother}}\end{array}\)

Therefore, the multiple regression equation is

\({\rm{Son}} = 18.0 + 0.504\;{\rm{Father}} + 0.277\;{\rm{Mother}}\).

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Most popular questions from this chapter

In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal).

If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why?

Prediction Interval Using the heights and weights described in Exercise 1, a height of 180 cm is used to find that the predicted weight is 91.3 kg, and the 95% prediction interval is (59.0 kg, 123.6 kg). Write a statement that interprets that prediction interval. What is the major advantage of using a prediction interval instead of simply using the predicted weight of 91.3 kg? Why is the terminology of prediction interval used instead of confidence interval?

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.)

CPI and the Subway Use CPI>subway data from the preceding exercise to determine whether there is a significant linear correlation between the CPI (Consumer Price Index) and the subway fare.

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?

Interpreting\({R^2}\)For the multiple regression equation given in Exercise 1, we get \({R^2}\)= 0.928. What does that value tell us?

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