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Answer :
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.
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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.
