# Can logistic regression be used for continuous dependent variable?

Table of Contents

- 1 Can logistic regression be used for continuous dependent variable?
- 2 What type of data does a logistic regression allow us to work with?
- 3 When can logistic regression be used?
- 4 Why regression is used in logistic regression?
- 5 When should logistic regression be used?
- 6 Is it better to have continuous predictors in logistic regression?
- 7 What is an independent variable in logistic regression?

## Can logistic regression be used for continuous dependent variable?

The logit regression model is generally used as a method for estimating relationships in which the dependent variable is binary in nature, though it is also useful for estimation when the dependent variable is continuous but bounded on the unit intervals.

## What type of data does a logistic regression allow us to work with?

Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.

**Can you use logistic regression for regression?**

It is an algorithm that can be used for regression as well as classification tasks but it is widely used for classification tasks.

**Is logistic regression continuous?**

Logistic regression analysis is a statistical technique that describes the relationship between an independent variable (either continuous or not) and a dichotomic-dependent variable (or dummy variable; i.e. a variable with only two possible values: 0 = outcome absent and 1 = outcome present).

### When can logistic regression be used?

Logistic Regression is used when the dependent variable(target) is categorical. For example, To predict whether an email is spam (1) or (0) Whether the tumor is malignant (1) or not (0)

### Why regression is used in logistic regression?

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.

**When can you not use logistic regression?**

Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space.

**When would you not use logistic regression?**

#### When should logistic regression be used?

#### Is it better to have continuous predictors in logistic regression?

This can cut two ways, but mostly one. In logistic regression, as with any flavour of regression, it is fine, indeed usually better, to have continuous predictors. Given a choice between a continuous variable as a predictor and categorising a continuous variable for predictors, the first is usually to be preferred.

**What are the three types of logistic regression?**

The three types of logistic regression are: Binary logistic regression is the statistical technique used to predict the relationship between the dependent variable (Y) and the independent variable (X), where the dependent variable is binary in nature. For example, the output can be Success/Failure, 0/1, True/False, or Yes/No.

**How can I calculate the target variable for a logistic regression?**

To accomplish it, just divide your score by 100, and run logistic regression with this [0,1] – based target variable, like in this question – you can do it, for example, with R, using I don’t know whether this approach helps with outliers – it depends on the sort of outliers you are expecting.

## What is an independent variable in logistic regression?

Independent variables are those variables or factors which may influence the outcome (or dependent variable). So: Logistic regression is the correct type of analysis to use when you’re working with binary data.