College

Logistic regression is used when you want to:

A. Predict a dichotomous variable from continuous or dichotomous variables.
B. Predict a continuous variable from dichotomous variables.
C. Predict any categorical variable from several other categorical variables.
D. Predict a continuous variable from dichotomous or continuous variables.

Answer :

Answer:

A. Predict a dichotomous variable from continuous or dichotomous variables.

Step-by-step explanation:

Logistic regression is used when you want to predict a dichotomous variable from continuous or dichotomous variables.

Mathematically, it is given by the expression;

Logistic regression [tex] y [/tex] with [tex] x_{1} [/tex], [tex] x_{2} [/tex]........[tex] x_{n} [/tex]

Where;

y represents the dichotomous dependent variable.

[tex] x_{1} [/tex], [tex] x_{2} [/tex]........[tex] x_{n} [/tex] represents the predictable variables, which are categorical in nature such as alive or dead, win or lose, sick or healthy, pass or fail, etc.

Final answer:

Logistic regression is used to predict a dichotomous variable using continuous or dichotomous variables, fitting the model with a logistic function to estimate probabilities of binary outcomes.

Explanation:

Logistic regression is employed in situations where the predictive analysis involves a dichotomous variable, which is a binary outcome (e.g., win/lose, success/failure, yes/no). It can use both continuous and dichotomous independent variables to predict the outcome.

Therefore, the correct answer to the student's question is A. Predict a dichotomous variable from continuous or dichotomous variables.

Unlike other regression models that predict a continuous variable, logistic regression is designed for categorical outcomes and it estimates the probability that a given input point belongs to a certain class.

The output is transformed using a logistic function, often called a sigmoid function, which ensures the output ranges between 0 and 1, representing the probability of the binary outcome.