why in models such as logistic regression we need to estimate the marginal effects of each independent variable in order to understand how a change in one independent variable affects our dependent variable.

Marginal effects can be used to express how the predicted probability of a binary outcome changes with a change in a risk factor. For example, how does 1-year mortality risk change with a 1-year increase in age or for a patient with diabetes compared with a patient without diabetes

Logistic regression, also known as logit regression, logit model, or just logit, is one of the most regression analyses taught at universities and used in data analysis. It is a non-linear model which predicts the outcome of a categorical dependent variable with respect to a vector of independent variables. When performing a logit regression with a statistical package, such as Stata, R or Python, the coefficients are usually provided by log-odds scale. In short, this means that point estimates are complicated to interpret, however the sign and the confidence interval of estimates can be interpreted. For that reason, it is interesting to interpret the logit model in the probability scale, i.e. as probabilities.

Marginal effect:

Marginal effects measure the impact that an instantaneous change in one variable has on the outcome variable while all other variables are held constant. In the simple OLS model with linear effects, estimated coefficients are always equal to marginal effects.

Independent Variable:

An Independent variable is exactly what it sounds like. It is a variable that stands alone and isn’t changed by the other variables you are trying to measure. For example, someone’s age might be an independent variable. Other factors (such as what they eat, how much they go to school, how much television they watch) aren’t going to change a person’s age. In fact, when you are looking for some kind of relationship between variables you are trying to see if the independent variable causes some kind of change in the other variables, or dependent variables.

Dependent Variable:

Just like an independent variable, a dependent variable is exactly what it sounds like. It is something that depends on other factors. For example, a test score could be a dependent variable because it could change depending on several factors such as how much you studied, how much sleep you got the night before you took the test, or even how hungry you were when you took it. Usually when you are looking for a relationship between two things you are trying to find out what makes the dependent variable change the way it does.

Many people have trouble remembering which is the independent variable and which is the dependent variable. An easy way to remember is to insert the names of the two variables you are using in this sentence in they way that makes the most sense. Then you can figure out which is the independent variable and which is the dependent variable:

(Independent variable) causes a change in (Dependent Variable) and it isn’t possible that (Dependent Variable) could cause a change in (Independent Variable).

Marginal effectscan be used to express how the predictedprobabilityof abinary outcomechanges with a change in a risk factor. For example, how does 1-year mortality risk change with a 1-year increase in age or for a patient with diabetes compared with a patient without diabetesLogistic regression, also known as logit regression, logit model, or just logit, is one of the mostregression analysestaught at universities and used indata analysis. It is anon-linear modelwhich predicts the outcome of a categorical dependent variable with respect to a vector ofindependent variables.When performing a logit regression with a statistical package, such as Stata, R or Python, the coefficients are usually provided by log-oddsscale. In short, this means that point estimates are complicated to interpret, however the sign and the confidence interval of estimates can be interpreted. For that reason, it is interesting to interpret the logit model in theprobability scale, i.e. as probabilities.Marginal effect:Marginal effectsmeasure the impact that an instantaneous change inone variablehas on theoutcome variablewhile all other variables are held constant. In the simple OLS model with linear effects, estimatedcoefficientsare always equal to marginal effects.Independent Variable:Independent variableis exactly what it sounds like. It is avariablethat stands alone and isn’t changed by the other variables you are trying to measure. For example, someone’s age might be anindependent variable.Other factors (such as what they eat, how much they go to school, how much television they watch) aren’t going to change a person’s age. In fact, when you are looking for some kind of relationship between variables you are trying to see if the independent variable causes some kind of change in the othervariables, or dependent variables.Dependent Variable:independent variable, adependent variableis exactly what it sounds like. It is something that depends on other factors. For example, atest scorecould be a dependent variable because it could change depending on several factors such as how much you studied, how much sleep you got the night before you took the test, or even how hungry you were when you took it. Usually when you are looking for a relationship between two things you are trying to find out what makes the dependent variable change the way it does.variablesyou are using in this sentence in they way that makes the most sense. Then you can figure out which is the independent variable and which is the dependent variable:Regression: