Is logistic regression more appropriate than linear regression for a binary response variable?

Is logistic regression more appropriate than linear regression for a binary response variable?

Linear regression is used when the dependent(output/outcome) variable is continuous. Whereas, Logistics regression is used when the dependent variable is categorical(binary).

Why is a linear regression model inappropriate for data with binary response select all apply?

In binary regression, it would be inappropriate to put a line through the observations in a data plot, because the values of the independent variables can only be 0 or 1. Regression is method of verifying causal relationships.

Can you do a regression with binary dependent variable?

In particular, we consider models where the dependent variable is binary. We will see that in such models, the regression function can be interpreted as a conditional probability function of the binary dependent variable.

Why is logistic regression better at modeling a binary outcome?

Logistic Regression uses a different method for estimating the parameters, which gives better results–better meaning unbiased, with lower variances.

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What is binary linear regression?

In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output binary variable. Generally the probability of the two alternatives is modeled, instead of simply outputting a single value, as in linear regression.

Which of the following is a problem of ordinal regression with a binary dependent variable?

the proportional odds assumption. a normally distributed outcome variable. equal group variances. independence of irrelevant alternatives.

Why is linear regression not suitable for classification problems?

There are two things that explain why Linear Regression is not suitable for classification. The first one is that Linear Regression deals with continuous values whereas classification problems mandate discrete values. The second problem is regarding the shift in threshold value when new data points are added.

Why linear regression is not suitable for time series?

The main argument against using linear regression for time series data is that we’re usually interested in predicting the future, which would be extrapolation (prediction outside the range of the data) for linear regression. Extrapolating linear regression is seldom reliable.

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