When to use binary variable or continuous variable?
We never create dummy variables for continuous features. Ff you are making a prediction for continuous response variable then use the linear regression but if the response variable is binary then you should try logistic regression.
Which is better binary data or continuous data?
In general, binary data provide less information than an equivalent amount of continuous data. If you can collect continuous data, it’s the better route to take! Poisson Hypothesis Tests for Count Data Count data can have only non-negative integers (e.g., 0, 1, 2, etc.).
When to use binary variables in linear regression?
If you have reason to believe that the standard linear regression assumptions, y = X β + ϵ, with ϵ being IID normal, and etc. Then it doesn’t matter that your predictors are continuous or binary. Binary outcomes should generally not be modeled with a linear regression.
How to correlate dichotomous variable to continuous variable?
When used to correlate a dichotomous variable and a continuous variable, it is actually equivalent to doing a t-test on that continuous variable over the dichotomous one. (The t-statistic and p-value will come out the same).
Can a logistic regression be both discrete and continuous?
Just like in any ordinary linear regression, the covariates may be both discrete and continuous. The basic principle for logistic regression is the same whether covariates are discrete or continuous, but some adjustments are necessary for goodness-of-fit testing.
When to use influence function in Proc logistic regression?
In SAS, under PROC LOGISTIC, INFLUENCE option and IPLOTS option will output these diagnostics. In R see, influence() function. As pointed out before, the standardized residual values are considered to be indicative of lack of fit if their absolute value exceeds 3 (some sources are more conservative and take 2 as the threshold value).