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Advanced Inferential Statistics

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Advance Inferential Statistics

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Understand the concepts of Binary Logistic Regression Logistic regression is essential in predicting a categorical variable present in a set of variables that are predictors. During Logistic regression, categorical dependent variable which is the discriminant function is used when all the predictors that are present are continuous and distributed in a nice way. The binary logic regression has become the most preferred data analysis method that describes the relationship between response variable and an explanatory variable that and it is usually used where a variable follows binomial distribution.
Assumptions of binary logistic regression Among the assumptions applied in the application of binary logistic regression is that logistic regression usually does not assume a relationship that is linear between the dependable and undependable variables. It is a must for the dependable variable to be a dichotomy i.e. must have two categories. It is also not required that the independent variables to be an interval, distributed in a normal way, linearly related, or even have linear variance within specific groups. Another assumption is that large samples are most important in this regression because it has a maximum likelihood of coefficients of large sample estimates. It is usually recommended that one should have a linear regression of 50 cases and above per predictor.
Requirements for Binary Logistic Regression When carrying out binary logistics it is required that the outcome variables should have one success and one failure. There has to be a way of estimating the probability of P relating to the outcome variable. Logistic regression requires a link between the outcome variable and the explanatory variable. Coefficients of the regression equation

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