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What happens when you change the dependent variable?
The dependent variable (sometimes known as the responding variable) is what is being studied and measured in the experiment. It’s what changes as a result of the changes to the independent variable.
Does the dependent variable have to change?
Dependent variables are variables whose changes depend solely on another variable—usually the independent variable. That is, the value of the dependent variable will only change if the independent variable changes.
When are dependent variables are not fit for linear models?
In my study, the dependent variable is dichotomous, because of that I used binary logistic regression to analyze the data (Spss program). I got the results, but beta coefficients do not make sense because the values are greater than 1. now I am struggling with transforming beta coefficient to meaningful values.
What kind of model do you use for ordinal variables?
Like unordered categorical variables, ordinal variables require specialized logistic or probit models, such as the proportional odds model. There are a few other types of ordinal models, but the proportional odds model is most commonly available.
What happens when irrelevant variables are omitted from a regression model?
If relevant variables are omitted from the model, the common variance they share with included variables may be wrongly attributed to those variables, and the error term can be inflated. On the other hand, if irrelevant variables are included in the model, the common variance they share with included variables may be wrongly attributed to them.
Why are discrete counts not fit for linear models?
Discrete counts fail the assumptions of linear models for many reasons. The most obvious is that the normal distribution of linear models allows any value on the number scale, but counts are bounded at 0.