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What is the difference between multiple regression and logistic regression?
In cases wherethe dependent variable can take any numerical value for a given set of independent variables multiple regression is used.But in cases when the dependent variable is qualitative(dichotomous,polytomous)then logistic regression is used.In Multiple regression the dependent variable is assumed to follow normal distribution but in case of logistic regression the dependent variablefollows bernoulli distribution(if dichotomous) which means it will be only0 or 1.
What is the difference between the logistic regression and regular regression?
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
What are the advantages and disadvantage of logistic regression compared with linear regression analysis?
It all depends on what data set you're working with. There a quite a number of different regression analysis models that range the gambit of all functions you can think of. Obviously some are more useful than others. Logistic regression is extremely useful for population modelling because population growth follows a logistic curve. The final goal for any regression analysis is to have a mathematical function that most closely fits your data, so advantages and disadvantages depend entirely upon that.
What is the minimum required sample size for running logistic regression?
In fitting a logistic regression, as in applying any statistic method, the required sample size depends on the level of dispersion in the population and the quality of the statistics that you are prepared to accept. Usually there will be some information available somewhere (in the literature, say, or from colleagues) suggesting what level of variability to expect in data that is collected. This can be used to simulate some data sets and the logistic regression results that would arise from them. By varying the sizes of the data sets you can make a judgement. Once you have collected your first sample and fit the actual logistic regression to it you will have a much better idea how much data is actually required for useful estimates.
How would you evaluate a logistic regression model?
A subsection of the question above. You have to demonstrate an understanding of what the typical goals of a logistic regression are (classification, prediction, etc.) and bring up a few examples and use cases.
