Logistic regression is used when you want to: Answer choices 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.Logistic regression is used when you want to: Answer choices 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.

Question

09-03-2021
Mathematics

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Logistic regression is used when you want to: Answer choices 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.Logistic regression is used when you want to: Answer choices 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.

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Lanuel Lanuel · Answer 1 · 2021-03-13T08:10:37+01:00

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.