Intro Stats + New Mylab Statistics With Pearson Etext:
5th Edition
ISBN: 9780134210230
Author: De Veaux, Richard D.
Publisher: Pearson College Div
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Chapter 9, Problem 6E
a.
To determine
Explain what the histogram tells about the linearity condition for the regression.
b.
To determine
Explain what the histogram tells about the near normal condition for the regression.
c.
To determine
Explain what the histogram tells about the equal spread condition for the regression.
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Chapter 9 Solutions
Intro Stats + New Mylab Statistics With Pearson Etext:
Ch. 9.4 - Recall the regression example in Chapter 7 to...Ch. 9.4 - Prob. 2JCCh. 9.4 - Prob. 3JCCh. 9 - Housing prices The following regression model was...Ch. 9 - Candy sales A candy maker surveyed chocolate bars...Ch. 9 - Prob. 3ECh. 9 - Prob. 4ECh. 9 - Prob. 5ECh. 9 - Prob. 6ECh. 9 - Movie profits once more Look back at the...
Ch. 9 - Prob. 8ECh. 9 - Prob. 9ECh. 9 - More indicators For each of these potential...Ch. 9 - Interpretations A regression performed to predict...Ch. 9 - Prob. 12ECh. 9 - Prob. 13ECh. 9 - Scottish hill races Hill runningraces up and down...Ch. 9 - Prob. 15ECh. 9 - Candy bars per serving: calories A student...Ch. 9 - Prob. 17ECh. 9 - More hill races Here is the regression for the...Ch. 9 - Prob. 19ECh. 9 - Home prices II Here are some diagnostic plots for...Ch. 9 - Admin performance The AFL-CIO has undertaken a...Ch. 9 - GPA and SATs A large section of Stat 101 was asked...Ch. 9 - Prob. 23ECh. 9 - Breakfast cereals We saw in Chapter 7 that the...Ch. 9 - Breakfast cereals again We saw a model in Exercise...Ch. 9 - Prob. 26ECh. 9 - Hand dexterity Researchers studied the dexterity...Ch. 9 - Candy bars with nuts The data on candy bars per...Ch. 9 - Scottish hill races, men and women The Scottish...Ch. 9 - Scottish hill races, men and women climbing The...
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- Cellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions in the United States from 2008 through 2013. Source: CTIA- The Wireless Association Year200820092010201120122013Number,y270286296316326336 (a) Find the least squares regression line for the data. Let x represent the year, with x=8 corresponding to 2008. (b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part a? (c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.arrow_forwardZipfs Law The following table shows U.S cities by rank in terms of population and population in thousands. City Rank r Population N New York 1 8491 Chicago 3 2722 Philadelphia 5 1560 Dallas 9 1280 Austin 11 913 San Francisco 13 852 Columbus 15 836 A rule known as Zipfs law tells us that it is reasonable to approximate these data with a power function. a Use power regression to express the population as a function of the rank. b Plot the data along with the power function from part a. c Phoenix is the sixth largest city in the United States. Use your answer from part a to estimate population of Phoenix. Round your answer in thousands to the nearest whole number. Note: The actual population is 1537 thousand.arrow_forwardA Dubious Model of Oil Prices The following table shows the prices of oil in U.S. dollars per barrel, t years since 1990, One analysis involving additional data used a cubic equation to model this data. t Years since 1990 0 2 5 7 10 12 15 17 20 21 P Price, dollars per barrel 18.91 16.22 16.63 18.20 27.04 23.47 49.63 69.04 77.46 106.92 a. Use cubic regression to model these data. Round the regression parameters to four decimal places. b. Plot the data along with the cubic model. c. In the analysis mentioned above, the graph is expanded through 2020. Expand the viewing window to show the model from 1990 to 2020. d. What estimate does the model give for oil prices in 2015? e. The actual price of oil in December of 2015 was about 35 per barrel. What basic principle in the use of models would be violated in relying on the estimate in part d?arrow_forward
- XYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ Corporation in the given month. Month Stock price January 2011 43.71 February 2011 44.22 March 2011 44.44 April 2011 45.17 May 2011 45.97 a. Find the equation of the regression line. Round the regression coefficients to three decimal places. 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 the stock price to be in January 2012? January 2013?arrow_forwardAdjusted Gross Income An individuals adjusted gross income is the amount of income that is subject to federal income tax. The following table shows the total adjusted gross income AGI, in trillions of dollars, reported to the IRS in the given year. Year 2009 2010 2011 2012 AGI trillions 7.6 8.1 8.6 9.1 a. Show that the data are linear. b. Let t denote the time in years since 2009, and let A denote the total adjusted gross income. Find a linear model for A as a function of t. c. Identify the slope of the linear model you found in part b, and explain its meaning in practical terms.arrow_forwardFurther Verification of Newtons Second LawThis exercise represents a hypothetical implementation of the experiment suggested in the solution of part 6 of Example 3.7. A mass of 15 kilograms was subjected to varying accelerations, and the resulting force was measured. In the following table, acceleration is in meters per second per second, and force is in newton. Acceleration Force 8 120 11 165 14 210 17 255 20 300 a. Construct a table of differences and explain how it shows that these data are linear. b. Find a linear model for the data. c. Explain in practical terms what the slope of this linear model is. d. Express, using functional notation, the force resulting from an acceleration of 15 meters per second per second, and then calculate that value. e. Explain how this experiment provides further evidence for Newtons second law of motion.arrow_forward
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