Concept explainers
Finding the Equation of a Regression Line In Exercises 17–26, find the equation of the regression line for the data. Then construct a
17. Height and Number of Stories The heights (in feet) and the numbers of stories of the nine tallest buildings in Houston, Texas (Source: Empires Corporation)
(a) x = 950 feet
(b) x = 850 feet
(c) x = 800 feet
(d) x = 700 feet
Trending nowThis is a popular solution!
Chapter 9 Solutions
Elementary Statistics: Picturing the World (7th Edition)
- 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. CPI and the Subway Use the CPI/subway fare data from the preceding exercise and find the best predicted subway fare for a time when the CPI reaches 500. What is wrong with this prediction?arrow_forwardCity Fuel Consumption: Finding the Best Multiple Regression Equation. 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). A Honda Civic weighs 2740 lb, it has an engine displacement of 1.8 L, and its highway fuel consumption is 36 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate?arrow_forwardCity Fuel Consumption: Finding the Best Multiple Regression Equation. 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). Which regression equation is best for predicting city fuel consumption? Why?arrow_forward
- City Fuel Consumption: Finding the Best Multiple Regression Equation. 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 exactly two predictor (x) variables are to be used to predict the city fuel consumption, which two variables should be chosen? Why?arrow_forwardRegression establishes functional relationship between and variable. a. Cross and Managerial O b. Mutual and Managerial O C. Average and Mutual O d. Independent and dependentarrow_forwardConstruct a scatter plot of the data in the table AND create a linear model. 2 10 12 10 14 14 16 18 18 24 a EGO (99- 2.arrow_forward
- Heptathlon 2004 again We saw the data for the wom-en’s 2004 Olympic heptathlon in Exercise 63. Are the two jumping events associated? Perform a regressionof the long-jump results on the high-jump results.a) What is the regression equation? What does the slopemean?b) What percentage of the variability in long jumps canbe accounted for by high-jump performances?c) Do good high jumpers tend to be good long jumpers?d) What does the residuals plot reveal about the model?e) Do you think this is a useful model? Would you useit to predict long-jump performance? (Compare theresidual standard deviation to the standard deviationof the long jumps.)arrow_forwardConsider the image a plot of a regression line. What do you call the regression line that results in the smallest sum of errors squared? a. correlational lineb. forecasting linec. least squares regression lined. Pearson's linearrow_forwardIn linear regression, the dependent variables are the O a. Regressors |b. Responses O C. Fitted Regression Line o d. Coefficient of determinationarrow_forward
- 1. In an attempt to assess the effect of the length of time spent in using social media to the academic performance of the student, a survey was conducted to ten randomly selected students to determine the number of minutes per week using social media and their scores in a department examination in mathematic which consists of a 50-item test. The data are as follows:arrow_forwardLife Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forwardObserve the point in red in the scatterplot below. Estimate the effect the point would have on a linear regression model based on its presence. Scatterplot of y vs xarrow_forward
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillFunctions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage LearningBig Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt