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Use properties of logarithms to condense each logarithmic expression. Write the expression as a single logarithm whose coefficient is \(1 .\) Where possible, evaluate logarithmic expressions without using a calculator. $$ 4 \ln (x+6)-3 \ln x $$

Short Answer

Expert verified
\[ \ln \frac {(x+6)^4}{x^3} \]

Step by step solution

01

Apply the Power Rule for Logarithms

By using the power rule, the expression can be rewritten as: \( \ln (x+6)^4 - \ln x^3 \)
02

Apply the Quotient Rule for Logarithms

Using the quotient rule, simplify the expression into a single logarithm: \( \ln \frac {(x+6)^4}{x^3} \)
03

Simplify the Expression

The resultant expression is already a single logarithm with a coefficient of 1, as required. Therefore, no further simplification is needed

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Key Concepts

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

Properties of Logarithms
Logarithms, much like exponents, have unique properties that simplify complex expressions. These properties allow us to break down or combine logarithmic terms. Understanding these properties can greatly simplify calculations that use logs. The main properties of logarithms include the Product Rule, the Quotient Rule, and the Power Rule.
  • Product Rule: This states that the logarithm of a product is the sum of the logarithms of the factors. Mathematically, \( \log_b (MN) = \log_b M + \log_b N \).
  • Quotient Rule: This is applicable when you're dealing with the logarithm of a division or quotient: \( \log_b \left( \frac{M}{N} \right) = \log_b M - \log_b N \).
  • Power Rule: This simplifies a logarithm with an exponent: \( \log_b (M^n) = n \cdot \log_b M \).
These properties are fundamental in manipulating and simplifying logarithmic expressions, especially when transforming them into a format that is more manageable for calculations. Each property builds upon the concept of logs as the inverse of exponentiation.
Power Rule for Logarithms
The Power Rule is particularly helpful when dealing with logarithmic expressions that involve exponents. It allows the exponent to "come down" and multiply the logarithm, making the expression easier to manage. This rule is typically used in the initial steps of simplifying a logarithmic expression.For example, in the expression \( 4 \ln (x+6) \), we can apply the Power Rule. Here's how it works:1. You see that the number "4" in front of the logarithm can be turned into an exponent: \( \ln (x+6)^4 \).2. This transformation makes it easier to further simplify the expression.The Power Rule is simple but powerful because it converts a multiplication problem into a power one, making subsequent steps like using the Quotient Rule more straightforward. Understanding and applying the Power Rule correctly is crucial when simplifying expressions involving multiple logarithmic components.
Quotient Rule for Logarithms
The Quotient Rule provides an efficient way to handle the division of numbers within a logarithm. It is hugely beneficial for condensing logarithmic expressions into a single log term. This is particularly useful when you have expressions that involve subtractions of logs.When applying the Quotient Rule, keep in mind:
  • The rule states that \( \log_b \left( \frac{M}{N} \right) = \log_b M - \log_b N \).
  • This means you can take two logarithmic expressions with a subtraction operation and condense them into one logarithm representing the division of their arguments.
In the exercise, after using the Power Rule to rewrite \( \ln (x+6)^4 \) and \( \ln x^3 \), you can see how the Quotient Rule simplifies the expression:1. Start with \( \ln (x+6)^4 - \ln x^3 \).2. Apply the Quotient Rule to get \( \ln \frac{(x+6)^4}{x^3} \).The Quotient Rule effectively consolidates terms, allowing you to achieve an expression as a single logarithm. This makes it easier to evaluate or further manipulate, particularly when dealing with complex or multiple logarithmic terms.

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Most popular questions from this chapter

In Example I on page \(520,\) we used two data points and an exponential function to model the population of the United States from 1970 through 2010 . The data are shown again in the table. Use all five data points to solve Exercises \(66-70\). $$ \begin{array}{cc} {x, \text { Number of Years }} & {y, \text { U.S. Population }} \\ {\text { after } 1969} & {\text { (millions) }} \\ {1(1970)} & {203.3} \\ {11(1980)} & {226.5} \\ {21(1990)} & {248.7} \\ {31(2000)} & {281.4} \\ {41(2010)} & {308.7} \end{array} $$ Use your graphing utility's logarithmic regression option to obtain a model of the form \(y=a+b \ln x\) that fits the data. How well does the correlation coefficient, \(r,\) indicate that the model fits the data?

Use your graphing utility to graph each side of the equation in the same viewing rectangle. Then use the \(x\) -coordinate of the intersection point to find the equation's solution set. Verify this value by direct substitution into the equation. $$ \log _{3}(4 x-7)=2 $$

Determine whether each statement makes sense or does not make sense, and explain your reasoning. I used an exponential model with a positive growth rate to describe the depreciation in my car's value over four years.

Students in a mathematics class took a final examination. They took equivalent forms of the exam in monthly intervals thereafter. The average score, \(f(t),\) for the group after \(t\) months was modeled by the human memory function \(f(t)=75-10 \log (t+1), \quad\) where \(\quad 0 \leq t \leq 12 .\) Use a graphing utility to graph the function. Then determine how many months elapsed before the average score fell below 65.

In Example I on page \(520,\) we used two data points and an exponential function to model the population of the United States from 1970 through 2010 . The data are shown again in the table. Use all five data points to solve Exercises \(66-70\). $$ \begin{array}{cc} {x, \text { Number of Years }} & {y, \text { U.S. Population }} \\ {\text { after } 1969} & {\text { (millions) }} \\ {1(1970)} & {203.3} \\ {11(1980)} & {226.5} \\ {21(1990)} & {248.7} \\ {31(2000)} & {281.4} \\ {41(2010)} & {308.7} \end{array} $$ Use the values of \(r\) in Exercises \(66-69\) to select the two model= of best fit. Use each of these models to predict by which yeathe U.S. population will reach 335 million. How do these answers compare to the year we found in Example \(1,\) namel \(=\) \(2020 ?\) If you obtained different years, how do you account fo this difference?

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