(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.

Note: Revolving credit is mostly credit card and home equity loans, while nonrevolving credit is for a specific purchase such as a car.

Consumer Credit Outstanding, 2000–2010 ($ billions)
Year	Total	Revolving	Nonrevolving
2000	1,722	683	1,039
2001	1,872	716	1,155
2002	1,984	749	1,235
2003	2,088	771	1,317
2004	2,202	801	1,401
2005	2,296	827	1,469
2006	2,385	871	1,514
2007	2,522	942	1,580
2008	2,561	958	1,604
2009	2,449	866	1,584
2010	2,403	796	1,607

Use the table below for Exercise. Attendance and Revenue at U.S. Theme Parks

$$\begin{matrix} \text{Year} & \text{1990} & \text{1992} & \text{1994} & \text{1996} & \text{1998} & \text{2000} & \text{2002} & \text{2004} & \text{2006}\\ \text{Attendance (millions)} & \text{253} & \text{267} & \text{267} & \text{290} & \text{300} & \text{317} & \text{324} & \text{328} & \text{335}\\ \text{Revenue (billions of dollars)} & \text{5.7} & \text{6.5} & \text{7.0} & \text{7.9} & \text{8.7} & \text{9.6} & \text{9.9} & \text{10.8} & \text{11.5}\\ \end{matrix}$$

Make a scatter plot of the data pairs (year, attendance). Draw a trend line and write its equation. Estimate the attehdance at U.S. theme parks In 2005.

The technical report “Ozone Season Emissions by State” (U.S. Environmental Protection Agency, 2002) gave the following nitrous oxide emissions (in thousands of tons) for the 48 states in the continental U.S. states: Use these data to construct a boxplot that shows outliers. Write a few sentences describing the important characteristics of the boxplot.

$$\begin{array}{lllllllllll}{76} & {22} & {40} & {7} & {30} & {5} & {6} & {136} & {72} & {33} & {0} \\ {89} & {136} & {39} & {92} & {40} & {13} & {27} & {1} & {63} & {33} & {60} \\ {27} & {16} & {63} & {32} & {20} & {2} & {15} & {36} & {19} & {39} & {130} \\ {40} & {4} & {85} & {38} & {7} & {68} & {151} & {32} & {34} & {0} & {6} \\ {43} & {89} & {34} & {0} & {} & {} & {} & {}\end{array}$$

The median income for a four-person family for selected years is:

$$\begin{matrix} & \text{Median Income}\\ \text{Year} & \text{1988} & \text{1990} & \text{1992} & \text{1994} & \text{1996} & \text{1998} & \text{2000} & \text{2003}\\ & \text{Income} & \text{\$ 39.050} & \text{\$ 41.150} & \text{\$44.250} & \text{\$ 47.010} & \text{\$ 51.500} & \text{\$ 56.050} & \text{\$ 62.200} & \text{\$ 65.100}\\ \end{matrix}$$

(a) Use these data to find the least squares linear	function with income as a function of years since 1988. (b) Use the linear function to estimate the median	income for 1985. (The actual was $32,800.) (c) Use the linear function to estimate the median income for 2002. (The actual was$62,700.) (d) Based on the linear function, when will the median income reach $75,000?

U.S. Mail The data in the following table show an increase in the number of pieces of mail delivered by the U.S. Postal Service from 2002 to 2006.

$$\begin{array}{|c|c|} \hline \text { Year, } \boldsymbol{x} & \begin{array}{c} \text { Number of Pieces of } \\ \text { Mail (in billions) } \end{array} \\ \hline 2002,0 & 203 \\ 2003,1 & 202 \\ 2004,2 & 206 \\ 2005,3 & 212 \\ 2006,4 & 213 \\ \hline \end{array}$$

a) Without using a graphing calculator, model the data with a linear function and, using this function, predict the number of pieces of mail delivered in 2010. Answers may vary depending on the data points used. b) Using a graphing calculator, fit a regression line to the data and use it to predict the number of pieces of mail delivered in 2010 . What is the correlation coefficient for the regression line?

The file Silver contains the following prices in London for an ounce of silver (in US$) on the last day of the year from 1999 to 2012:$

$$\begin{array}{cc|cc} \textbf{Year} & \textbf{Salary (\$ millions)} & \textbf{Year} & \textbf{Salary (\$ millions)}\\ \hline 2000 & 1.99 & 2007 & 2.92\\ 2001 & 2.29 & 2008 & 3.13\\ 2002 & 2.38 & 2009 & 3.26\\ 2003 & 2.58 & 2010 & 3.27\\ 2004 & 2.49 & 2011 & 3.32\\ 2005 & 2.63 & 2012 & 3.38\\ 2006 & 2.83 & 2013 & 4.25\\ \end{array}$$

$

a. Plot the data.

b. Compute a linear trend forecasting equation and plot the trend line.

c. Compute a quadratic trend forecasting equation and plot the results.

d. Compute an exponential trend forecasting equation and plot the results.

e. Which model is the most appropriate?

f. Using the most appropriate model, forecast the price of silver at the end of 2013.

Prescription Drug Sales. The table below lists the percentages of brand-name drugs and generic drugs sold in recent years.

$$\begin{array}{|c|c|c|} \hline \text { Year } & \begin{array}{c} \text { Percentage of } \\ \text { Brand-Name Drugs } \\ \text { Sold } \end{array} & \begin{array}{c} \text { Percentage of } \\ \text { Generic Drugs } \\ \text { Sold } \end{array} \\ \hline 1995 & 59.8 & 40.2 \\ 2000 & 57.6 & 42.4 \\ 2002 & 57.9 & 42.1 \\ 2004 & 51.9 & 48.1 \\ \hline \end{array}$$

a) Find linear functions $b(x)$ and $g(x)$ that represent the percentages of brand-name drugs and generic drugs sold, respectively, $x$ years after 1995. b) Use the functions found in part (a) to estimate when the percentages of brand-name drugs and generic drugs sold will be equal.

Fill in the missing numbers in the following table. Change decimal answers to percents and round to the nearest tenth of a percent.

$$\begin{matrix} \text{Year} & \text{Noninstitutional} & \text{Employed} & \text{Unemployed} & \text{Civilian} & \text{Unemployment} & \text{Employment}\\ \text{ } & \text{adult civilian population} & \text{ } & \text{ } & \text{labor force} & \text{rate} & \text{rate}\\ \text{2002} & \text{15000} & \text{8178} & \text{522} & \text{ } & \text{ } & \text{ }\\ \text{2006} & \text{15250} & \text{8000} & \text{ } & \text{8390} & \text{ } & \text{ }\\ \text{2010} & \text{15750} & \text{ } & \text{614} & \text{9500} & \text{ } & \text{ }\\ \text{2014} & \text{16000} & \text{9400} & \text{ } & \text{10000} & \text{ } & \text{ }\\ \end{matrix}$$

Find the coefficients of the regression line to two decimals unless otherwise noted. The following table gives the life expectancy at birth, for selected years, of females born in the United States:

$$\begin{matrix} \text{Year} & \text{To Age}\\ \text{1940} & \text{65.2}\\ \text{1960} & \text{73.1}\\ \text{1980} & \text{77.4}\\ \text{1996} & \text{79.1}\\ \text{2000} & \text{79.7}\\ \text{2002} & \text{79.9}\\ \end{matrix}$$

(a) Using x=0 for 1940, x=20 for 1960, and so on, find the least squares line that fits these data. (b) Using the least squares line, find the estimated life expectancy of a female born in 2010. (c) Use the least squares line to estimate when a newborn’s life expectancy will reach 100.

The article “The Healthy Kids Survey: A Look at the Findings” (San Luis Obispo Tribune, Oc to ber 25, 2002) gave the accompanying information for a sample of fifth graders in San Luis Obispo County. Responses are to the question: “After school, are you home alone without adult supervision?” a. Summarize these data using a pie chart. b. Construct a segmented bar graph for these data. c. Which graphing method—the pie chart or the segmented bar graph—do you think does a better job of conveying information about response? Explain.

$$\begin{matrix} \text{Response} & \text{Percentage}\\ \text{Never} & \text{8}\\ \text{Some of the time} & \text{15}\\ \text{Most of the time} & \text{16}\\ \text{All of the time} & \text{61}\\ \end{matrix}$$

The average salaries (in thousands of dollars) for football players in the National Football League from 2000 to 2004 are shown in the table.

The coefficients of the least squares regression parabola $y=a t^2+b t+c$, where y represents the average salary (in thousands of dollars) and t represents the year, with t=0 corresponding to 2000 , can be found by solving the system

$$\left\{\begin{aligned} 5 c+10 b+30 a & =5,543 \\ 10 c+30 b+100 a & =12,447 \\ 30 c+100 b+354 a & =38,333 \end{aligned}\right.$$

Do you believe the model can be used to predict the average salaries for future years Explain.

The table shows the population of Florida for 2000 and 2010, with estimates given by the U.S. Census Bureau for 2001 through 2009.

$$\begin{matrix} \text{Year} & \text{2000} & \text{2001} & \text{2002} & \text{2003} & \text{2004} & \text{2005}\\ \text{Population in millions} & \text{15.98} & \text{16.24} & \text{16.50} & \text{16.76} & \text{17.03} & \text{17.30}\\ \text{Year} & \text{2006} & \text{2007} & \text{2008} & \text{2009} & \text{2010}\\ \text{Population in millions} & \text{17.58} & \text{17.86} & \text{18.15} & \text{18.44} & \text{18.80}\\ \end{matrix}$$

Divide the population, for each year by the population in the preceding year. Round to two decimal places and show that Florida has a population increase that is approximately geometric.

This exercise is based on the following table, which shows some values of ABCromD (ABCD) stock:

$$\begin{matrix} \text{Dec. 2002} & \text{Aug. 2004} & \text{Mar. 2005} & \text{May 2005} & \text{Aug. 2005} & \text{Dec. 2005}\\ \text{3.28} & \text{16.31} & \text{33.95} & \text{21.00} & \text{30.47} & \text{7.44}\\ \text{Jan. 2007} & \text{Mar. 2008} & \text{Oct. 2008} & \text{Nov. 2009} & \text{Feb. 2010} & \text{Aug. 2010}\\ \text{12.36} & \text{7.07} & \text{11.44} & \text{33.53} & \text{44.86} & \text{45.74}\\ \end{matrix}$$

John O’Hagan purchased ABCD stock in March 2005 and sold it in January 2007. Calculate his annual return on a simple interest basis to the nearest 0.01%.

Nina owns a shoe store. These tables display data about the shoe store.

Table A Sizes of Shoes Sold in May

$$\begin{array}{|c|c|} \hline Size & Number \space of \space Pairs \space Sold \\ \hline 6 & 60 \\ \hline 7 & 239 \\ \hline 8 & 217 \\ \hline 9 & 156 \\ \hline 10 & 61 \\ \hline 11 & 43 \\ \hline 12 & 36 \\ \hline \end{array}$$

Table B Yearly Sales

$$\begin{array}{|c|c|} \hline Year & Sales (\$) \\ \hline 2000 & 579000 \\ \hline 2001 & 621000 \\ \hline 2002 & 598000 \\ \hline 2003 & 634000 \\ \hline 2004 & 657000 \\ \hline 2005 & 642000 \\ \hline 2006 & 675000 \\ \hline \end{array}$$

a) Which data would be best represented with a line graph? Explain your choice.

b) Which type of graph would be appropriate for the other table? Justify.