Chapter 4: Problem 84
Use the properties of natural logarithms to rewrite the expression. $$7 \ln e^{0}$$
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Chapter 4: Problem 84
Use the properties of natural logarithms to rewrite the expression. $$7 \ln e^{0}$$
These are the key concepts you need to understand to accurately answer the question.
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Solve the equation algebraically. Round the result to three decimal places. Verify your answer using a graphing utility. $$2 x^{2} e^{2 x}+2 x e^{2 x}=0$$
An exponential model of the form \(y=a b^{x}\) can be rewritten as a natural exponential model of the form _________.
Use a graphing utility to approximate the point of intersection of the graphs. Round your result to three decimal places. $$\begin{array}{l}y_{1}=3.25 \\\y_{2}=\frac{1}{2} \ln (x+2)\end{array}$$
The populations \(P\) (in thousands) of Luxembourg for the years 1999 through 2013 are shown in the table, where \(t\) represents the year, with \(t=9\) corresponding to 1999. (Source: European Commission Eurostat) $$\begin{array}{|c|c|}\hline \text { Year } & \text { Population, } P \\\\\hline 1999 & 427.4 \\\2000 & 433.6 \\\2001 & 439.0 \\\2002 & 444.1 \\\2003 & 448.3 \\\2004 & 455.0 \\\2005 & 461.2 \\\2006 & 469.1 \\\2007 & 476.2 \\\2008 & 483.8 \\\2009 & 493.5 \\\2010 & 502.1 \\\2011 & 511.8 \\\2012 & 524.9 \\\2013 & 537.0 \\\\\hline\end{array}$$ (a) Use the regression feature of a graphing utility to find a linear model for the data and to identify the coefficient of determination. Plot the model and the data in the same viewing window. (b) Use the regression feature of the graphing utility to find a power model for the data and to identify the coefficient of determination. Plot the model and the data in the same viewing window. (c) Use the regression feature of the graphing utility to find an exponential model for the data and to identify the coefficient of determination. Plot the model and the data in the same viewing window. (d) Use the regression feature of the graphing utility to find a logarithmic model for the data and to identify the coefficient of determination. Plot the model and the data in the same viewing window. (e) Which model is the best fit for the data? Explain. (f) Use each model to predict the populations of Luxembourg for the years 2014 through 2018. (g) Which model is the best choice for predicting the future population of Luxembourg? Explain. (h) Were your choices of models the same for parts (e) and \((g) ?\) If not, explain why your choices were different.
The table shows the lengths \(y\) (in centimeters) of yellowtail snappers caught off the coast of Brazil for different ages (in years). (Source: Brazilian Journal of Oceanography) $$\begin{array}{|c|c|}\hline \text { Age, }x & \text { Length, } y\\\\\hline 1 & 11.21 \\\2 & 20.77 \\\3 & 28.94 \\\4 & 35.92 \\\5 & 41.87 \\\6 & 46.96 \\\7 & 51.30 \\\8 & 55.01 \\\9 & 58.17 \\\10 & 60.87 \\\11 & 63.18 \\\12 & 65.15 \\\13 & 66.84 \\\14 & 68.27 \\\15 & 69.50 \\\\\hline\end{array}$$ (a) Use the regression feature of a graphing utility to find a logistic model and a power model for the data. (b) Use the graphing utility to graph each model from part (a) with the data. Use the graphs to determine which model better fits the data. (c) Use the model from part (b) to predict the length of a 17-year-old yellowtail snapper.
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