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Exercises 13鈥28 use the same data sets as Exercises 13鈥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.

Use the pizza costs and subway fares to find the best predicted

subway fare, given that the cost of a slice of pizza is $3.00. Is the best predicted subway fare likely to be implemented?

Short Answer

Expert verified

The regression equation is\(\hat y = - 0.0111 + 1.01x\).

The best-predicted 鈥榮ubway fare鈥 for the cost of a slice of pizza is $3.00 will be approximately $3.02. The best-predicted subway fare of $3.02 is not likely to be implemented due to its convenience to use in a real situation.

Step by step solution

01

Given information

The given data provides the information of the pizza cost (in dollars) and subway fare as follows.

02

State the estimated regression line

The formula for the estimated regression line is

\(y = {b_0} + {b_1}x\).

Here,

\({b_0}\)is the Y-intercept,

\({b_1}\)is the slope,

\(x\)is the explanatory variable, and

\(\hat y\)is the response variable (predicted value).

Let X denotes the cost of a pizza slice (in dollars) and Y denote the subway fare (in dollars).

03

Compute the slope and intercept

The calculations required to compute the slope and intercept are as follows.

The sample size is \(\left( n \right) = 9\).

The slope is computed as follows.

\(\begin{array}{c}{b_1} = \frac{{n\left( {\sum {xy} } \right) - \left( {\sum x } \right)\left( {\sum y } \right)}}{{n\left( {\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}}}\\ = \frac{{9 \times 27.8325 - 13.8 \times 13.85}}{{9 \times 27.685 - {{13.8}^2}}}\\ = 1.010856\end{array}\).

The intercept is computed as follows.

\(\begin{array}{c}{b_0} = \frac{{\left( {\sum y } \right)\left( {\sum {{x^2}} } \right) - \left( {\sum x } \right)\left( {\sum {xy} } \right)}}{{n\left( {\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}}}\\ = \frac{{13.8 \times 27.685 - 13.8 \times 27.8325}}{{9 \times 27.685 - {{13.8}^2}}}\\ = - 0.01109\end{array}\).

Thus, the estimated regression equation is

\(\begin{array}{c}\hat y = {b_0} + {b_1}x\\ = - 0.011 + 1.012x\end{array}\).

04

Check the model

Refer to exercise 15 of section 10-1 for the following result.

1) The scatter plot shows an approximate linear relationship between the variables.

2)The P-value is 0.000.

As the P-value is less than the level of significance (0.05), the null hypothesis is rejected.

Therefore, the correlation is statistically significant.

Referring to figure 10-5, the criteria for a good regression model are satisfied.

Thus, the prediction is made using a regression equation.

05

Compute the prediction

The best-predicted subway fare for the cost of a slice of pizza of $3.00 is required.

Therefore, the estimated value for $3.00 is

\(\begin{array}{c}\hat y = {b_0} + {b_1}x\\ = - 0.0111 + 1.01x\\ = - 0.0111 + 1.01 \times 3\\ \approx 3.02\end{array}\).

Therefore, the best-predicted subway fare for the cost of a slice of pizza, which is $3.00, will be approximately $3.02.

The prediction of $3.02 is not likely to be possible as the denomination is not a convenient value, as compared to values like $3.25 or $3.00.

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Most popular questions from this chapter

Testing for a Linear Correlation. In Exercises 13鈥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.)

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Lemon Imports

230

265

358

480

530

Crash Fatality Rate

15.9

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In Exercises 9鈥12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 鈥淐ar Measurements鈥 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).

Which regression equation is best for predicting city fuel consumption? Why?

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

Variation and Prediction Intervals. In Exercises 17鈥20, find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. In each case, there is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions.

Altitude and Temperature Listed below are altitudes (thousands of feet) and outside air temperatures (掳F) recorded by the author during Delta Flight 1053 from New Orleans to Atlanta. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet).

Altitude (thousands of feet)

3

10

14

22

28

31

33

Temperature (掳F)

57

37

24

-5

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-41

-54

Regression and Predictions. Exercises 13鈥28 use the same data sets as Exercises 13鈥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.

Using the diameter/circumference data, find the best predicted circumference of a marble with a diameter of 1.50 cm. How does the result compare to the actual circumference of 4.7 cm?

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