We transfer æ into x* = log(æ) and fit a linear model y = ax* + b when –1 < x* < 1. What is the correct model for the original variable x and y? O y = a log(x) + b when 1/e < x < e O y = a exp(æ)+b when -e
We transfer æ into x* = log(æ) and fit a linear model y = ax* + b when –1 < x* < 1. What is the correct model for the original variable x and y? O y = a log(x) + b when 1/e < x < e O y = a exp(æ)+b when -e
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...
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Transcribed Image Text:We transfer x into x* = log(x) and fit a linear model y = ax* +b when –1 < x* < 1. What is the correct model for the original variable x and y?
O y = a log(x)+b when 1/e < æ < e
O y = a exp(x) +b when -e < x <e
O y = a exp(x)+ b when 1/e < æ < e
O y = a log(c) + b when -e < x < e
![What is the major difference between random forests and bagged trees?
O Only random forests use Bootstrap sampling.
O Only random forests bag multiple trees to make predictions.
O Only random forests bag multiple de-correlated trees to make predictions.
O Only random forests use tree-based models.
Given a logistic regression model logit[Pr(Y = 1|x)] = Bx + Bo. If x = 0, what is the value of Pr(Y = 1)?
O exp(80)/[1+ exp(B)]
O Bo
O exp(Bo)
O 1/1+ exp(Bo)]
We transfer a into a* = log(x) and fit a linear model y = ax* + b when -1 < x* < 1. What is the correct model for the original variable x and y?
O y = a log(x)+b when 1/e < x < e
Oy = a exp(x) +b when -e < x <e
O y = a exp(x) +b when 1/e < a <e
O y = a log(x) +b when -e < x <e](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F57d40d2a-6ef8-486b-8e28-6d5ae9c2c97b%2F9c86ef5c-6ecd-4053-bb36-e662be907b5b%2Fuztn7hh_processed.jpeg&w=3840&q=75)
Transcribed Image Text:What is the major difference between random forests and bagged trees?
O Only random forests use Bootstrap sampling.
O Only random forests bag multiple trees to make predictions.
O Only random forests bag multiple de-correlated trees to make predictions.
O Only random forests use tree-based models.
Given a logistic regression model logit[Pr(Y = 1|x)] = Bx + Bo. If x = 0, what is the value of Pr(Y = 1)?
O exp(80)/[1+ exp(B)]
O Bo
O exp(Bo)
O 1/1+ exp(Bo)]
We transfer a into a* = log(x) and fit a linear model y = ax* + b when -1 < x* < 1. What is the correct model for the original variable x and y?
O y = a log(x)+b when 1/e < x < e
Oy = a exp(x) +b when -e < x <e
O y = a exp(x) +b when 1/e < a <e
O y = a log(x) +b when -e < x <e
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