An article considered regressing y = 28-day standard-cured strength (psi) against x = accelerated strength (psi). Suppose the equation of the true regression line is y = 1900 + 1.3x, and that the standard deviation of the random deviation e is 350 psi. n USE SALT (a) What is the probability that the observed value of 28-day strength will exceed 5000 psi when the value of accelerated strength is 2100? (Round your answer to four decimal places.) (b) What is the probability that the observed value of 28-day strength will exceed 5000 psi when the value of accelerated strength is 2400? (Round your answer to four decimal places.) (c) Consider making two independent observations on 28-day strength, the first for an accelerated strength of 2100 and the second for x = 2400. What is the probability that the second observatio will exceed the first by more than 1000 psi? (Round your answer to four decimal places.) (d) Let Y, and Y, denote observations on 28-day strength when x = x, and x = x2, respectively. By how much would x, have to exceed x, in order that P(Y, > Y,) = 0.95? (Round your answer to two decimal places.) this auuoction

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

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?