The true statement is that Maximum Likelihood Estimation is used to estimate the parameters of a logistic regression model. This method finds the model parameters that make the observed data most probable without prior information.
The statement that is true is: d. Maximum Likelihood Estimation is the estimation method by which we find the intercept and slope for a logistic regression model.
Maximum Likelihood Estimation (MLE) is a method used in statistics to estimate the parameters of a statistical model. In the context of logistic regression, MLE chooses the values for the intercept and slope (coefficients) that make the observed data most probable. This method does not assume any prior information on the parameter values.
While the generalized linear model (GLM) encompasses a range of models including logistic regression, they are not the same thing. Adding family=binomial to GLM when running a logistic regression in R is not optional; it specifies that the model should use the binomial distribution, which is necessary for a logistic regression. Moreover, GLMs do require assumptions to be met, including the correct specification of the link function and the distribution of the response variable.