The Helicopter Division of Aerospatiale is studying assembly costs at its Marseilles plant. Past data indicates the accompanying data of number of labor hours per helicopter. Reduction in labor hours over time is often called a "learning curve" phenomenon. Using these data, apply simple linear regression and examine the esidual plot. What do you conclude? Construct a scatter chart and use the Excel Trendline feature to identify the best type of curvilinear trendline (but not going beyond a second-order polynomial) that maximizes R. Click the icon to view the Helicopter Data. The residuals plot has a nonlinear shape. Therefore, this data cannot be modeled with a linear model. Determine the best curvilinear trendline that maximizes R. Data table for number of hours per helicopter OA. The best trendline is Logarithmic with an R² value of The equation is y (D tn ( . TT (Round the coefficient of the logarithm to one decimal place as needed. Round all other values to three decimal places as needed.) Helicopter Number Labor HourS 2000 1400 1238 OB. The best trendline is Exponential with an R value of The equation is y= Oe. (Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.) ос. 3 The best trendline is Power with an R value of. The equation is y= (Dx. (Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.) 1142 1075 1029 985 957 O D. The best trendline is Polynomial with an R? value of The equation is y D.+ (Round to three decimal places as needed.) Print Done

Does Table 1 represent a linear function? If so, finda linear equation that models the data.

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?

What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?