Consider the following scatterplot of amounts of CO (carbon monoxide) and NOX (nitrogen oxide) in grams per mile driven in the exhausts of cars. The least-squares regression line has been drawn in the plot. 2.0 -0.1 -0.7 Amount of NOX (grams/mile) What is the approximate value of the intercept of the least-squares regression line? O a 1.8 Ob Oc Od Oe 18 2.25 2.00 1.75 1.50 1.25- 1.00- 0.75- 0.50- 0 T 2 4 6 8 10 12 14 16 Amount of CO (grams/mile)

Can the average rate of change of a function be constant?

If you travel 100 miles in two hours, then your average speed for the trip is Average speed=_________=________

Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?

The number of banks in a country has been dropping steadily since 1984, and the trend in recent years has been roughly linear. The annual data for the years 1999 through 2008 can be summarized as follows, where x represents the years since 1990 and y the number of banks, in thousands. n=10 Ex-235 ²-5605 Ey-77.564 Ey²=603.80424 Exy-1810.155 a. Find an equation for the least squares line. b. Use your result from part a to predict the number of banks in the year 2020. c. If this trend continues linearly, in what year will the number of banks drop below 6200? d. Find and interpret the correlation coefficient.

Here are prices and screen sizes (in inches, measured diagonally) for 7 different sizes of one brand of LED HD television. Use technology to calculate the equation of the least-squares regression line relating y = price to x = screen size. Enter the regression equation below. Round the slope and y-intercept to 2 decimal places. Screen size Price 60.           1000 55.           800 50            700 47.           600 42.           430 39            400 32            300

Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package measured in ounces. You want to try to predict the cost of a typical shipment given package dimensions. If 10 packages in a city are sampled and the regression output is given below, report the regression equation. 1)  (cost of delivery) = 1.468*(weight) - 23.015   2)  (weight) = -23.015*(cost of delivery) + 1.468   3)  (cost of delivery) = -23.015*(weight) + 1.468   4)  (cost of delivery) = 1.468*(weight)   5)  (weight) = 1.468*(cost of delivery) - 23.015

The scatter plot below shows the average cost of a designer jacket in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x.     A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate.    Interpret the slope of the least squares regression line. Select the correct answer below:   1.The average cost of a designer jacket decreased by $3.765 each year between 2000 and 2015.   2.The average cost of a designer jacket increased by $3.765 each year between 2000 and 2015.   3.The average cost of a designer jacket decreased by $4815 each year between 2000 and 2015.   4. The average cost of a designer jacket increased by $4815 each year between 2000 and…

Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Intercept Source Coefficients Total df 11 3.996 SS F Regression 1 9.858769 9.858769 12.22345 Residual 8.872 0.806545 0.358 Save for Later Standard Error Using a=0.05, Louis should 12 18.73077 O suspend judgment O not reject Ho: Po=0 O reject Ho: 1₁=0 O increase the sample size O not reject Ho: 1-0 p-value 1.161268 3.441065 0.004885 0.102397 3.496205 0.004413 t Statistic MS Se = 0.898 r² = 0.526341