*5. Population The table shows the approximate populations of the United States for (116) certain decades. Year 1930 1940 | 1960 | 1980 || 1990 || 2000 Population (millions) 1.23 1.32 1.79 2.27 2.49 2.81 a. List the following types of regressions in order from those of best fit to worst fit for the data: linear, quadratic, cubic, quartic, logarithmic, and exponential. b. Use the first model listed for the answer in part a to estimate the population of the United States in 1950.

*5. Population The table shows the approximate populations of the United States for (116) certain decades. Year 1930 1940 | 1960 | 1980 || 1990 || 2000 Population (millions) 1.23 1.32 1.79 2.27 2.49 2.81 a. List the following types of regressions in order from those of best fit to worst fit for the data: linear, quadratic, cubic, quartic, logarithmic, and exponential. b. Use the first model listed for the answer in part a to estimate the population of the United States in 1950.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.1: Logistic Functions

Problem 17E: World Population The following table shows world population N, in billions, in the given year. Year...

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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?
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Special Rounding instructions When you perform logistic regression, round the r value to three decimal places and other parameters to two decimal places. Round all answers to two decimals places unless otherwise indicated. Ebola Figure 5.14 shows a plot of the total number E of Ebola cases in West Africa reported to the centers for Diseases control and Prevention from April 1, 2014, through December 1, 2015. A logistic fit for these data is given by, E=27,841.421+134.65e0.646t. Where t is the time in months since April 1, 2014. In figure 5.15, we have added the graph of this model. a.Does Figure 5.14 show a continuing epidemic or a health crisis that is under control by December 2015? b.According to the model, what was the total number of cases when the disease was spreading at the fastest rate? Round your answer to the nearest whole number. c.Use the crossing- graphs method to determine when the disease was growing at the fastest rate. Round your answer, in months, to one decimal place.
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