Since the logit transformation (14.18) linearizes the logistic response function, why can't this transformation be used on the individual responses Yi and a linear response function then fitted? Explain

Since the logit transformation (14.18) linearizes the logistic response function, why can't this transformation be used on the individual responses Yi and a linear response function then fitted? Explain

Name: Since the logit transformation (14.18) linearizes thelogistic respons
Uploaded: 2024-03-20T07:06:27.000Z
Channel: Bartleby
Description: Since the logit transformation (14.18) linearizes thelogistic response function, why can't thistransformation be used on the individual responsesYi and a linear response function thenfitted? Explain

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 5SE: What does the y -intercept on the graph of a logistic equation correspond to for a population...

See similar textbooks

Similar questions

What is the y -intercept on the graph of the logistic model given in the previous exercise?
Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.
Suppose you trained your logistic regression classifier which takes an image as input and outputs either dog (class 0) or cat (class 1). Given the input image x, the hypothesis outputs 0.2. What is the probability that the input image corresponds to a dog?
A researcher collects data that represents the average number of hours of sleep in the last two nights by 8 depressed patients and 9 non-depressed patients. The researcher is interested in whether the two groups reliably differ in the amount of sleep they get. Use Jamovi to calculate t-obt and the p value.
For the 23 space shuttle flights before the Challenger mission disaster in 1986, The following table shows the temperature (◦F) at the time of the flight and whether at least one primary O-ring suffered thermal distress. Use logistic regression to model the effect of temperature on the probability of thermal distress. Interpret the effect. Estimate the probability of thermal distress at 31◦F, the temperature at the time of the Challenger flight. At what temperature does the estimated probability equal 0.50? At that temperature, give a linear approximation for the change in the estimated probability per degree increase in temperature. Interpret the effect of temperature on the odds of thermal distress. Test the hypothesis that temperature has no effect, using (i) the Wald test, (ii) the likelihood-ratio test.
A jar contains 7 gold, 5 silver, 4 blue, 3 red, and 1 green marbles. Two marbles are to be randomly drawn from the jar. What is the probability a sliver marble is drawn, not returned to the jar, and then a blue marble is drawn? Given the power regression model y = 25x^1.2, which is the linear regression model after transforming to a log-log graph?
Suppose a logistic regression analysis of some data for restaurants gave the following result, in terms of L, which here represents the logit, that is, the log odds, lnOdds. L(x1, x2) = .3 − .2x1 + .1x2, where Y = 1 denotes closing within one year of startup,Y = 0 denotes staying in business more than one year , X1 = 1 if franchised, 0 if not,X2 = 1 if a fast-food restaurant, 0 if not,Px = Pr{Y = 1|x1,x2}, Qx = Pr{Y = 0|x1,x2},L(x1, x2) = ln(Px1,x2 /Qx1,x2 ) = logit(Px1,x2 ) 1. What is the value of L(0, 1) ? (A) .1 (B) .2 (C) .3 (D) .4 (E) .5 2. The number P(Y = 1|X1 = 0,X2 = 1) is the probability that (A) a franchised fast-food restaurant is bankrupt within one year. (B) a franchised non-fast food restaurant is not bankrupt within one year. (C) a non-franchised fast-food restaurant is bankrupt within one year. (D) a non-franchised non-fast food restaurant is bankrupt within one year. (E) a fast-food restaurant is franchised. 3. (continuation) What is the value of…
What is the Dirichlet ruler function?
Explain which characteristic of the STA leads to a consideration of a logistic model as opposed to a linear regression mode.
Why would the male lifespan not be the dependent variable?
The scatterplot of these two variables reveals a potential outlying month when the average temperature is about 53◦F and average crawling age is about 28.5 weeks. (a) Does this point have high leverage? (b) Is it an influential point?
The following table contains statistics from a logistic regression analysis for a study on intravenous drug use among high school students in United States. Drug use is characterized as a dichotomous variable, where 1 indicates that an individual has injected drugs within the past year and 0 that he or she has not. Factors that might be related to drug use are instruction about the HIV in school (1 represents "had HIV education" and 0 represents "did not have HIV education"), age of the student (in years), and gender (1 represents male and 0 represents female). Statistics in the table are estimated coefficients of the logistic regression model and p-values for testing the significance of the coefficients. Choose proper answers using statistics in the following table. (THERE MAY BE MORE THAN ONE CORRECT ANSWER) Variable Coefficient p-value Intercept (Constant) -0.164 0.078 HIV instruction 0.019 0.928 Age 0.064 0.036 Gender 1.032 0.014…