IDNLearn.com makes it easy to find answers and share knowledge with others. Join our community to receive prompt and reliable responses to your questions from experienced professionals.
Sagot :
To model the data using a logarithmic function, we can use the form:
[tex]\[ y = a \ln(x) + b \][/tex]
where \( \ln(x) \) represents the natural logarithm of \( x \), and \( a \) and \( b \) are constants that we need to determine.
Given the data points:
[tex]\[ \begin{array}{|c|c|} \hline x & y \\ \hline 1 & 60 \\ \hline 2 & 54 \\ \hline 3 & 51 \\ \hline 4 & 50 \\ \hline 5 & 46 \\ \hline 6 & 45 \\ \hline 7 & 44 \\ \hline \end{array} \][/tex]
we need to find the best values for constants \( a \) and \( b \) that fit this logarithmic model to the given data points. After performing the necessary calculations and fitting the logarithmic model to the data, we determine the values of \( a \) and \( b \).
The fitted parameters are:
[tex]\[ a = -8.245225947626354 \][/tex]
[tex]\[ b = 60.04169738027974 \][/tex]
Therefore, the logarithmic function that models the provided data is:
[tex]\[ y = -8.245225947626354 \ln(x) + 60.04169738027974 \][/tex]
This function captures the relationship between [tex]\( x \)[/tex] and [tex]\( y \)[/tex] based on the given data points.
[tex]\[ y = a \ln(x) + b \][/tex]
where \( \ln(x) \) represents the natural logarithm of \( x \), and \( a \) and \( b \) are constants that we need to determine.
Given the data points:
[tex]\[ \begin{array}{|c|c|} \hline x & y \\ \hline 1 & 60 \\ \hline 2 & 54 \\ \hline 3 & 51 \\ \hline 4 & 50 \\ \hline 5 & 46 \\ \hline 6 & 45 \\ \hline 7 & 44 \\ \hline \end{array} \][/tex]
we need to find the best values for constants \( a \) and \( b \) that fit this logarithmic model to the given data points. After performing the necessary calculations and fitting the logarithmic model to the data, we determine the values of \( a \) and \( b \).
The fitted parameters are:
[tex]\[ a = -8.245225947626354 \][/tex]
[tex]\[ b = 60.04169738027974 \][/tex]
Therefore, the logarithmic function that models the provided data is:
[tex]\[ y = -8.245225947626354 \ln(x) + 60.04169738027974 \][/tex]
This function captures the relationship between [tex]\( x \)[/tex] and [tex]\( y \)[/tex] based on the given data points.
Thank you for participating in our discussion. We value every contribution. Keep sharing knowledge and helping others find the answers they need. Let's create a dynamic and informative learning environment together. Thanks for visiting IDNLearn.com. We’re dedicated to providing clear answers, so visit us again for more helpful information.