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Question:

(a) Plot both Swiss watch time series on the same graph. (b) Describe the trend (if any) and discuss possible causes. (c) Fit an exponential trend to each time series. (d) Interpret each fitted trend carefully. What conclusion do you draw? (e) Make forecasts for the next 3 years, using the linear trend model. Why might 2009 have been unusual? Explain.
(a) Plot both Swiss watch time series on the same graph. (b) Describe the trend (if any) and discuss possible causes. (c) Fit an exponential trend to each time series. (d) Interpret each fitted trend carefully. What conclusion do you draw? (e) Make forecasts for the next 3 years, using the linear trend model. Why might 2009 have been unusual? Explain.





Transcribed Image Text:

Swiss Watch Exports (thousands of units), 2005–2010 Year Mechanical Electronic 2005 3,368 3,757 4,213 4,316 3,737 4,938 20,996 21,109 21,679 21,784 17,956 21,177 2006 2007 2008 2009 2010



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> (a) Use Excel’s function =RAND() or Excel’s Data Analysis > Random Numbers to generate 100 uniformly distributed random numbers between 0 and 1. (b) Make a histogram of your sample and assess its shape. (c) Calculat

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> Analysis of a Detroit Marathon (n = 1,015 men, n = 150 women) produced the regression results shown below, with dependent variable Time (the marathon time in minutes) and predictors Age (runner’s age), Weight (runner’s

> An expert witness in a case of alleged racial discrimination in a state university school of nursing introduced a regression of the determinants of Salary of each professor for each year during an 8-year period (n = 423) with the following results, with

> A sports enthusiast created an equation to predict Victories (the team’s number of victories in the National Basketball Association regular season play) using predictors FGP (team field goal percentage), FTP (team free throw percentage)

> A researcher used stepwise regression to create regression models to predict Birth Rate (births per 1,000) using five predictors: LifeExp (life expectancy in years), InfMort (infant mortality rate), Density (population density per square kilometer), GDPC

> Using test data on 20 types of laundry detergent, an analyst fitted a regression to predict Cost Per Load (average cost per load in cents per load) using binary predictors Top Load (1 if washer is a top loading model, 0 otherwise) and Powder (if detergen

> A hospital emergency room analyzed n = 17,664 hourly observations on its average occupancy rates using six binary predictors representing days of the week and two binary predictors representing the 8-hour work shift (12 a.m.–8 a.m., 8 a.m.–4 p.m., 4 p.m.

> In a study of paint peel problems, a regression was suggested to predict defects per million (the response variable). The intended predictors were supplier (four suppliers, coded as binaries) and substrate (four materials, coded as binaries). There were

> Which one of the following is true? Why not the others? a. Histograms are useful for visualizing correlations. b. Pyramid charts are generally preferred to bar charts. c. A correlation coefficient can be negative.

> In a model of Ford’s quarterly revenue Total Revenue = β0 + β1 Car Sales + β2 Truck Sales + β3 SUVSales + ε, the three predictors are measured in number of units sold (not dollars). (a). Interpret each slope. (b). Would the intercept be meaningful? (c)

> If you are using time-series data, perform one or more tests for autocorrelation (visual inspection of residuals plotted against observation order, runs test, Durbin-Watson test). Is autocorrelation a concern? DATA SET A Mileage and Other

> Here are the ages of a random sample of 20 CEOs (chief executive officers) of Fortune 500 U.S. corporations. (a). Find the mean, median, and mode. (b). Discuss advantages and disadvantages of each of these measures of center for this data set. (c). F

> If you did not already do so, request a plot of residuals versus the fitted Y. Is heteroscedasticity a concern? DATA SET A Mileage and Other Characteristics of Randomly Selected Vehicles (n = 73, k = 4) O Mileage Obs Vehicle CityMPG Length

> If you did not already do so, request a histogram of standardized residuals and/or a normal probability plot. Do the residuals suggest non-normal errors? Explain. DATA SET A Mileage and Other Characteristics of Randomly Selected Vehicles (

> If you did not already do so, request leverage statistics. Are any observations influential? Explain. DATA SET A Mileage and Other Characteristics of Randomly Selected Vehicles (n = 73, k = 4) O Mileage Obs Vehicle CityMPG Length Width Wei

> (a). If you did not already do so, request a table of standardized residuals. (b). Are any residuals outliers (three standard errors) or unusual (two standard errors)? DATA SET A Mileage and Other Characteristics of Randomly Selected Vehi

> (a). If you did not already do so, rerun the regression requesting variance inflation factors (VIFs) for your predictors. (b). Do the VIFs suggest that multicollinearity is a problem? Explain. DATA SET A Mileage and Other Characteristics

> (a). Generate a correlation matrix for your predictors. Round the results to three decimal places. (b). Based on the correlation matrix, is collinearity a problem? DATA SET A Mileage and Other Characteristics of Randomly Selected Vehicles

> Use the standard error to construct an approximate prediction interval for Y. Based on the width of this prediction interval, would you say the predictions are good enough to have practical value? DATA SET A Mileage and Other Characteristi

> Use Excel’s Add Trendline feature to fit a linear regression to the scatter plot. Is a linear model credible? Midterm and Final Exam Scores for Business Statistics Students Fall Semester 2011 (n = 58 students) Midterm Exam Score &acir

> Based on the R2 and ANOVA table for your model, how would you describe the fit? DATA SET A Mileage and Other Characteristics of Randomly Selected Vehicles (n = 73, k = 4) O Mileage Obs Vehicle CityMPG Length Width Weight ManTran 3968 3583

> (a) Which p-values indicate predictor significance at α = .05? (b) Do the p-values support the conclusions you reached from the t tests? (c) Do you prefer the t test or the p-value approach? Why? DATA SET A Mileage and Other

> (a) Plot U.S. petroleum imports on a graph. (b) Describe the trend (if any) and discuss possible causes. (c) Fit both a linear and an exponential trend. (c) Interpret each fitted trend equation, explaining the implications. (d) Make a projection for 2010

> (a) Plot both men’s and women’s winning times on the same graph. (b) Fit a linear trend model to each series (men, women). (c) Use Excel’s option to forecast each trend graphically to 2040 (i.e., to p

> Do a two-tailed t test for zero slope for each predictor coefficient at α = .05. State the degrees of freedom and look up the critical value in Appendix D (or from Excel). Appendix D: Confidence Level Confidence Level 80 90

> Does a class break stimulate the pulse? Here are heart rates for a sample of 30 students before and after a class break. Research question: At α = .05, do the medians differ? Heart Rate before and after Class Break Student Before After

> Salaries of 30 randomly chosen individuals in the same occupation (only the first 3 and last 3 observations are shown). The data are from a salary equity study comparing two industries. Research question: Without assuming normality of the populations, is

> (a) Plot the data on law enforcement officers killed. (b) Describe the trend (if any) and discuss possible causes or anomalies in the data. (c) Would a fitted trend be helpful? Explain. (c) Make a forecast for 2009 using any method you like (including ju

> A cognitive retraining clinic assists outpatient victims of head injury, anoxia, or other conditions that result in cognitive impairment. Each incoming patient is evaluated to establish an appropriate treatment program and estimated length of stay (ELOS

> (a) Plot both men’s and women’s winning times on the same graph. (b) Fit a linear trend model to each series. From the fitted trends, will the times eventually converge? Hint: Ask Excel for forecasts (e.g., 20 years ah

> (a) Plot either receipts and outlays or federal debt and GDP (plot both time series on the same graph). (b) Describe the trend (if any) and discuss possible causes. (c) Fit a trend of your choice to each. (d) Interpret each fitted trend equation, explain

> (a) Choose one beverage category and plot the data. (b) Describe the trend (if any) and discuss possible causes. (c) Would a fitted trend be helpful? Explain. (d) Fit several trend models. Which is best, and why? If none is satisfactory, explain. (e) Mak

> (a) Plot the data on U.S. general aviation shipments. (b) Describe the pattern and discuss possible causes. (c) Would a fitted trend be helpful? Explain. (d) Make a similar graph for 1993–2008 only. Would a fitted trend be helpful in ma

> Which statement is correct? Why not the others? a. Likert scales are interval if scale distances are meaningful. b. Cross-sectional data are measured over time. c. A census is always preferable to a sample.

> (a) Choose one category of consumer credit and plot it. (b) Describe the trend (if any) and discuss possible causes. (c) Fit a trend model of your choice. (d) Make forecasts for 3 years (2011–2013), using a trend model of your choice.

> If freeway speeds are normally distributed with a mean of μ = 70 mph and σ = 7 mph, find the probability that the speed of a randomly chosen vehicle (a) exceeds 78 mph; (b) is between 65 and 75 mph; (c) is less than 70 mph.

> (a) Plot the voter participation rate. (b) Describe the trend (if any) and discuss possible causes. (c) Fit both a linear and a quadratic trend to the data. (d) Which model is preferred? Why? (e) Make a forecast for 2012, using a trend model of your choi

> (a) Plot the total minutes of TV viewing time per household. (b) Describe the trend (if any) and discuss possible causes. (c) Fit a linear trend to the data. (d) Would this model give reasonable forecasts? Would another trend model be better? Explain. (e

> (a) Make a line chart for JetBlue’s revenue. (b) Describe the trend (if any) and discuss possible causes. (c) Fit both a linear and an exponential trend to the data. (d) Which model is preferred? Why? (e) Make annual forecasts for 2011&

> Fertility rates (children born per woman) are shown for 27 EU member nations in 2 years. Research question: At α = .05, is there a significant rank correlation? Only the first 3 and last 3 nations are shown. Fertility Rates in EU Membe

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2.99

See Answer