Business Statistics Plus Pearson Mylab Statistics With Pearson Etext 3rd Edition Norean R Sharpe ,Richard D De Veaux ,Paul Velleman - Solutions

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=+e) What reservations, if any, do you have about the confidence intervals you made in parts c and d?
=+d) From partc, give a 95% confidence interval for the average decrease in the charges of cardholders who charged$2000 in January.
=+c) Give a 95% confidence interval for the average February charges of cardholders who charged $2000 in January.
=+b) How much, on average, will cardholders who charged$2000 in January charge in February?
=+a) Build a regression model to predict February charges from January charges.
=+60. Seasonal spending, part 2. Financial analysts know that January credit card charges will generally be much lower than those of the month before. What about the difference between January and the next month? Does the trend continue? The data set on the DVD contains the monthly credit card
=+e) What reservations, if any, do you have about the confidence intervals you made in parts c and d?
=+d) From partc, give a 95% confidence interval for the average decrease in the charges of cardholders who charged$2000 in December.
=+c) Give a 95% confidence interval for the average January charges of cardholders who charged $2000 in December.
=+b) How much, on average, will cardholders who charged$2000 in December charge in January?
=+a) Build a regression model to predict January spending from December’s spending.
=+59. Seasonal spending. Spending on credit cards decreases after the Christmas spending season (as measured by amount charged on a credit card in December). The data set on the DVD with the same name as this exercise contains the monthly credit card charges of a random sample of 99 cardholders.
=+c) Explain the meaning of the 95% confidence intervals in this context.
=+b) Explain the meaning of the 95% prediction intervals in this context.
=+a) Working with the data set on the DVD with the same name as this exercise, find a regression model showing the relationship between personal computer adoption in 2012 PC/100 2012 (the response variable) and personal computer adoption in 2000 PC/100 2000 (the predictor variable).
=+58. Global reach 2012. The Internet has revolutionized business and offers unprecedented opportunities for globalization. However, the ability to access the Internet varies greatly among different regions of the world. One of the variables the United Nations collects data on each year is
=+c) How has setting aside Iceland changed the regression model? How is it likely to affect the intervals discussed ina) and b)?
=+b) Explain the meaning of the 95% confidence intervals in this context.You can see the regression model in Exercise 54. The extraordinary point is Iceland. If we set it aside, the model looks like this Response variable is: Energy Use 2010 R squared = 88.6%s = 13.10 with 32 - 2 = 30 degrees of
=+a) Explain the meaning of the 95% prediction intervals in this context.
=+. Energy use and recession, part 2. Examine the regression and scatterplot showing the regression line, 95% confidence intervals, and 95% prediction intervals using 2006 and 2010 energy use (kg oil equivalent per $1000 GDP) for a sample of 31 countries first examined in Exercise 54. The response
=+b) Explain the meaning of the 95% confidence intervals in this context.c) Using a statistics program, identify any unusual observations, and discuss their potential impact on the regression.
=+a) Explain the meaning of the 95% prediction intervals in this context.
=+56. Male employment 2012. Here is a scatterplot showing the regression line, 95% confidence intervals, and 95%prediction intervals, using 2011 and 2012 male unemployment data for a sample of 33 nations. The response variable is the 2012-Male Rate, and the predictor variable is the 2011-Male
=+c) Using a statistics program, identify any unusual observations, and discuss their potential impact on the regression.
=+b) Explain the meaning of the 95% confidence intervals in this context.
=+a) Explain the meaning of the 95% prediction intervals in this context.
=+55. Youth employment 2012. Here is a scatterplot showing the regression line, 95% confidence intervals, and 95%prediction intervals, using 2012 youth unemployment data for a sample of 33 nations. The response variable is the Male Rate, and the predictor variable is the Female Rate. Male Rate 2012
=+d) What percentage of the variability in 2010 Energy Use is explained by 2006 Energy Use?
=+c) Test an appropriate hypothesis to determine if the association is significant.
=+b) Examine the residuals to determine if a linear regression is appropriate.
=+a) Find a regression model showing the relationship between 2010 Energy Use (reponse variable) and 2006 Energy Use (predictor variable).
=+54. Energy use and recession. The great recession of 2008 changed spending and energy use habits worldwide. Based on data collected from the United Nations Millennium Indicators Database related to measuring the goal of ensuring environmental sustainability, investigate the association between
=+s = 4,870 with 61 - 2 = 59 degrees of freedom Variable Coefficient SE(Coeff) t-Ratio P-Value Intercept 45.6898 4.849 9.42 60.0001 MSRP -0.000416 0.000159 -2.61 0.0114 M15_SHAR8696_03_SE_C15.indd 537 14/07/14 7:30 AM 538 CHAPTER 15 Inference for Regression 25 05 10 15 Highway Residual 30 35
=+53. All the efficiency money can buy 2013. A sample of 61 model-2013 cars from an online information service was examined to see how fuel efficiency (as highway mpg) relates to the cost (Manufacturer’s Suggested Retail Price in dollars) of cars. Here are displays and computer output:25 30 35
=+b) Do you think that a company’s assets serve as a useful predictor of their sales?
=+a) Is there a significant linear association between LogAssets and LogSales? Find the t-value and P-value to test an appropriate hypothesis and state your conclusion in context.
=+52. Assets and sales, part 2. The analyst in Exercise 28 realized the data were in need of transformation because of the nonlinearity between the variables. Economists commonly take the logarithm of these variables to make the relationship more nearly linear, and she did too. (These are base 10
=+b) Do you think predictions made by this regression will be very accurate? Explain.
=+a) Find the 95% prediction interval for the effectiveness of the video on a pitcher with an initial ability of 33 strikes.
=+51. Little league product testing, part 2. Using the same data provided in Exercise 48, answer the following questions.
=+) Find a 90% prediction interval for the Math score of the senior class president, if you know she scored 710 on the Verbal section.
=+a) Find a 90% confidence interval for the mean SAT Math score for all students with an SAT Verbal score of 500.
=+50. SAT scores, part 3. Consider the high school SAT scores data from Exercise 31 once more. The mean Verbal score was 596.30 and the standard deviation was 99.52.
=+b) Create a 95% prediction interval for the gas mileage you might get driving your new 3450-pound SUV, and explain what that interval means.
=+a) Create a 95% confidence interval for the average fuel efficiency among cars weighing 2500 pounds, and explain what your interval means.
=+49. Fuel economy and weight, part 3. Consider again the data in Exercise 29 about the fuel economy and weights of cars.
=+48. Little League product testing. Ads for a Little League instructional video claimed that the techniques would improve the performances of Little League pitchers. To test this claim, 20 Little Leaguers threw 50 pitches each, and we recorded the number of strikes. After the players
=+c) Compute and discuss the regression model.
=+b) Do you think there is a linear association between Attendance and Pitcher Age?
=+a) Examine a scatterplot of Attend/Game and PitchAge.Check the conditions for regression.
=+47. Old pitchers. Many factors may affect fans’ decision to go to a ball game. Is it possible that fans prefer teams with an older pitching staff?
=+c) Using the data on the DVD, find the regression with and without the outlier.M15_SHAR8696_03_SE_C15.indd 536 14/07/14 7:30 AM Exercises 537
=+b) What effect would this point have on the regression, had it been left with the rest of the data?
=+a) In words, what does the outlying point say about Zappos?
=+46. Job growth again. In Exercise 44, the company Zappos was omitted. Here is a scatterplot of the data with Zappos plotted as an x:–0.250–0.0 0.2 0.4 0.6 x–0.125 0 2010 Job Growth 2012 Job Growth 0.125
=+b) Explain why the R2 of this regression is higher than the R2 of the regression in Exercise 43.
=+a) Sketch what a scatterplot of Index2011 vs. Index2010 is likely to look like. You do not need to see the data.
=+45. Cost index again. In Exercise 43, we examined the Worldwide Cost of Living Survey cost of living index for the ten most expensive cities, whose 2011 indices range from Singapore’s 137 to Tokyo’s 161. Let’s add the ten least expensive cities to the data. Their 2011 indices range from 46
=+d) Do these results indicate that, in general, companies with a higher job growth in 2010 had a higher job growth in 2012? Explain.
=+c) Explain what the R-squared in this regression means.
=+b) Assuming that the assumptions for inference are satisfied, perform the hypothesis test and state your conclusion in context.
=+44. Job growth 2012. Fortune Magazine publishes the top 100 companies to work for every year. Among the information listed is the percentage growth in jobs at each company. The period from 2009 to 2011 was a difficult one for job growth in the United States. The output below shows the
=+d) Do these results indicate that, in general, cities with a higher index in 2010 had a higher index in 2011? Explain.
=+c) Explain what the R-squared in this regression means.
=+b) Perform the hypothesis test and state your conclusion in context.
=+43. Cost index 2011. The Worldwide Cost of Living Survey published by The Economist provides an index that expresses the cost of living in other cities as a percentage of the New York cost. For example, in 2011, the cost of living index in Tokyo was 161, which means that it was 61% higher than
=+b) Assuming that the assumptions for regression inference are reasonable, test the null hypothesis.c) State your conclusion.
=+42. Investment firm. Are profits related to the number of employees? An investment firm wants to manage the pension fund of a major New York retailer. To prepare their presentation to the retailer, the investment firm analysts used simple linear regression to model the relationship between the
=+f) Create a 95% confidence interval for the slope of the true line.g) Interpret your interval in this context.
=+e) What is the equation of the regression line?
=+d) Is the association strong? Explain.
=+c) Is there evidence of an association between screen size and battery life? Test an appropriate hypothesis and state your conclusion.
=+a) How many tablet computers were tested?b) Are the conditions for inference satisfied? Explain.
=+41. Tablet computers. In July 2013, cnet.com listed the battery life (in hours) and screen size (in inches) for a sample of tablet computers. We want to know if screen size(in inches) is associated with battery life. (reviews.cnet.com/8301-19736_7-20080768-251/cnet-updates-tablet-te
=+b) Last year, the drug manufacturer Eli Lilly, Inc., reported gross sales of $23 billion (that’s $23,000 million). Create a 95% prediction interval for the company’s profits, and interpret your interval in context.
=+a) Find a 95% confidence interval for the slope of the regression line. Interpret your interval in context.
=+40. More sales and profits. Using a statistics program, consider again the relationship between the sales and profits of Fortune 500 companies that you analyzed in Exercise 38.
=+b) For the cities studied, the mean population was 1.7 million people. The population of Boston is approximately 0.6 million people. Predict the mean ozone level for cities of that size with an interval in which you have 90%confidence.
=+39. Ozone, again. Using a statistics program, consider again the relationship between the population and ozone level of U.S. cities that you analyzed in Exercise 37.a) Give a 90% confidence interval for the approximate increase in ozone level associated with each additional million city
=+b) Do you think that a company’s sales serve as a useful predictor of its profits? Use the values of both R2 and s in your explanation.
=+38. Sales and profits. A business analyst was interested in the relationship between a company’s sales and its profits.She collected data (in millions of dollars) from a random sample of Fortune 500 companies and created the regression analysis and summary statistics shown. The assumptions
=+b) Do you think that the population of a city is a useful predictor of ozone level? Use the values of both R2 and s in your explanation.
=+a) We suspect that the greater the population of a city, the higher its ozone level. Is the relationship statistically significant? Assuming the conditions for inference are satisfied, test an appropriate hypothesis and state your conclusion in context.
=+37. Ozone. The Environmental Protection Agency is examining the relationship between the ozone level (in parts per million) and the population (in millions) of U.S. cities. Part of the regression analysis is shown.M15_SHAR8696_03_SE_C15.indd 534 14/07/14 7:30 AM Exercises 535 Dependent variable
=+a) Find a 90% confidence interval for the slope of the true line describing the association between Math and Verbal scores.b) Explain in this context what your confidence interval means.
=+36. SAT scores, part 2. Consider the high school SAT scores data from Exercise 31.
=+b) Explain in this context what your confidence interval means.
=+35. Fuel economy and weight, part 2. Consider again the data in Exercise 29 about the gas mileage and weights of cars.a) Create a 95% confidence interval for the slope of the regression line.
=+c) Examine the scatterplot corresponding to the regression for No Opinion. How does it change your opinion of the trend in “no opinion” responses? Do you think the true slope is negative as shown in the regression output?No Opinion Year 30 40 50 60 70 80 90 100 12 10 86 42
=+b) Assuming that the conditions for inference are satisfied, perform the hypothesis test and state your conclusion.
=+a) State the hypotheses about the slope (both numerically and in words) that describes how voters’ thoughts have changed about voting for a woman.
=+34. Female president. The Gallup organization has, over six decades, periodically asked the following question:If your party nominated a generally well-qualified person for president who happened to be a woman, would you vote for that person?We wonder if the proportion of the public who have
=+a) State the hypotheses about the slope.b) Perform the hypothesis test and state your conclusion in context.c) Using a statistics program and the data on the DVD, check the assumptions and conditions.
=+33. Football salaries 2013. Football owners are constantly in competition for good players. The more wins, the more likely that the team will provide good business returns for the owners. The resources that each of the 32 teams has in the National Football League (NFL) vary, but the draft system
=+b) Using the reciprocal measure, Hours per Output (000s), test an appropriate null hypothesis and state an appropriate conclusion (assume that assumptions and conditions are now met).
=+a) From a scatterplot, is there evidence of a linear association between Labor Productivity and Unit Labor Costs?Plot the reciprocal, Hours per output (000s), against Unit Labor Costs. Why did the analysts prefer this measure of productivity?
=+32. Productivity. How strong is the association between labor productivity and labor costs? Data from the Bureau of Labor Statistics for labor productivity, as measured by Output per Hour, and Unit Labor Costs across 53 industries, are used to examine this relationship
=+M15_SHAR8696_03_SE_C15.indd 533 14/07/14 7:30 AM 534 CHAPTER 15 Inference for Regression 10 20 15 5Residuals# of Students–180 –80 20 120 220a) Is there evidence of a linear association between Math and Verbal scores? Write an appropriate hypothesis.b) Discuss the assumptions for inference.c)
=+31. SAT scores. How strong was the association between student scores on the Math and Verbal sections of the old SAT? Scores on this exam ranged from 200 to 800 and were widely used by college admissions offices. Here are summary statistics, regression analysis, and plots of the scores for a
=+f) Create a 95% confidence interval for the slope of the true line.g) Interpret your interval in this context.

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Business Statistics Plus Pearson Mylab Statistics With Pearson Etext 3rd Edition Norean R Sharpe ,Richard D De Veaux ,Paul Velleman - Solutions

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