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Write the exponential equation in logarithmic form. For example, the logarithmic form of \(e^{2}=7.3890 . . .\) is \(\ln 7.3890 . . .=2.\) $$e^{1.3}=3.6692 . . .$$

Short Answer

Expert verified
\(\ln(3.6692...)=1.3\)

Step by step solution

01

Recognize the Exponential

Recognize the given exponential form which is \(e^{1.3}=3.6692 . . .\)
02

Convert to Logarithmic Form

Rewrite the equation in logarithmic form using the base \(e\), which is the base for the natural logarithm. In this case, it transforms into \(\ln(3.6692...)=1.3\)

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Natural Logarithm
The natural logarithm is a special type of logarithm with the base of Euler's number, denoted as \(e\). Unlike common logarithms, which use a base of 10, natural logarithms are based on \(e\), an irrational number approximately equal to 2.71828. The natural logarithm is often abbreviated as \(\ln\). Natural logarithms are deeply rooted in various branches of mathematics due to their natural appearance in sciences like biology and calculus, especially in growth models and compound interest calculations.

The beauty of the natural logarithm lies in how neatly it relates to exponential functions, which is why it's commonly used:
  • Simplifying complex calculus problems.
  • Solving exponential equations.
  • Transforming complex multiplication into addition.
Exponential Equation
An exponential equation is an equation where the variable appears in the exponent. When dealing with exponential equations, we often encounter a form such as \(e^x = y\). Here, \(e\) is the base and \(x\) is the exponent. Exponential equations are especially useful when modeling situations involving exponential growth or decay, such as population dynamics or radioactive decay.

Solving exponential equations typically involves converting the exponential form into its logarithmic counterpart, which allows us to isolate and solve for the exponent. For example, in the exercise provided, the equation \(e^{1.3} = 3.6692\) is an exponential equation, where "1.3" is the exponent and \(e\) is the base.
Base e
Euler's number, denoted as \(e\), is one of the most important constants in mathematics. It is approximately 2.71828 and is the base for natural logarithms. This number arises naturally in many contexts, especially where there are continuous growth processes or compounded processes over time. Because of its natural occurrence, it's used extensively in calculations involving growth and decay.

When you see expressions like \(e^x\), it's referring to an exponential function using \(e\) as the base. The choice of base \(e\) is natural and convenient in both mathematical theory and practical applications, such as calculating continuously compounded interest or during the analysis of differential equations.
Conversion Between Forms
Converting between exponential and logarithmic forms is a key skill in algebra and calculus. The basic principle behind this conversion is understanding that these forms are inverse operations. An exponential equation like \(a^b = c\) can be rewritten in logarithmic form as \(\log_a(c) = b\). This transformation is particularly useful when solving for an unknown exponent in an equation.

In the context of natural logarithms, you often see expressions converted such that \(e^x = y\) becomes \(\ln(y) = x\). This conversion allows us to express systems that are exponential in nature as linear equations, thereby simplifying the process of solving for the unknown.
  • Logarithmic forms help in solving equations where the unknown is an exponent.
  • They assist in simplifying multiplication into addition, aiding in manual calculations.
Converting between these forms opens up a world of techniques and simplifications in solving real-world problems efficiently.

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Most popular questions from this chapter

Determine whether the statement is true or false. Justify your answer. The exponential model \(y=a e^{b x}\) represents a growth model when \(b>0.\)

Use the regression feature of a graphing utility to find a logarithmic model \(y=a+b \ln x\) for the data and identify the coefficient of determination. Use the graphing utility to plot the data and graph the model in the same viewing window. $$(3,14.6),(6,11.0),(9,9.0),(12,7.6),(15,6.5)$$

Solve the equation algebraically. Round the result to three decimal places. Verify your answer using a graphing utility. $$3 x \ln \left(\frac{1}{x}\right)-x=0$$

A beaker of liquid at an initial temperature of \(78^{\circ} \mathrm{C}\) is placed in a room at a constant temperature of \(21^{\circ} \mathrm{C}\) The temperature of the liquid is measured every 5 minutes for a period of \(\frac{1}{2}\) hour. The results are recorded in the table, where \(t\) is the time (in minutes) and \(T\) is the temperature (in degrees Celsius). $$\begin{array}{|c|c|}\hline \text { Time, } t 0 & \text { Temperature, } T\\\\\hline 0 & 78.0^{\circ} \\\5 & 66.0^{\circ} \\\10 & 57.5^{\circ} \\\15 & 51.2^{\circ} \\\20 & 46.3^{\circ} \\\25 & 42.5^{\circ} \\\30 & 39.6^{\circ} \\\\\hline\end{array}$$ (a) Use the regression feature of a graphing utility to find a linear model for the data. Use the graphing utility to plot the data and graph the model in the same viewing window. Do the data appear linear? Explain. (b) Use the regression feature of the graphing utility to find a quadratic model for the data. Use the graphing utility to plot the data and graph the model in the same viewing window. Do the data appear quadratic? Even though the quadratic model appears to be a good fit, explain why it might not be a good model for predicting the temperature of the liquid when \(t=60.\) (c) The graph of the temperature of the room should be an asymptote of the graph of the model. Subtract the room temperature from each of the temperatures in the table. Use the regression feature of the graphing utility to find an exponential model for the revised data. Add the room temperature to this model. Use the graphing utility to plot the original data and graph the model in the same viewing window. (d) Explain why the procedure in part (c) was necessary for finding the exponential model.

The table shows the annual sales \(S\) (in billions of dollars) of Starbucks for the years from 2009 through 2013. (Source: Starbucks Corp.) $$\begin{array}{|c|c|}\hline \text { Year } & \text { Sales, } S \\\\\hline 2009 & 9.77 \\\\\hline 2010 & 10.71 \\\\\hline 2011 & 11.70 \\\\\hline 2012 & 13.30 \\\\\hline 2013 & 14.89 \\\\\hline\end{array}$$ (a) Use the regression feature of a graphing utility to find an exponential model for the data. Let \(t\) represent the year, with \(t=9\) corresponding to 2009. (b) Rewrite the model from part (a) as a natural exponential model. (c) Use the natural exponential model to predict the annual sales of Starbucks in \(2018 .\) Is the value reasonable?

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