# What type of variables are used in logistic regression?

### Table of Contents

- What type of variables are used in logistic regression?
- Can regression be used for continuous variables?
- Can you do logistic regression on categorical variables?
- Do you need to normalize variables for logistic regression?
- What are the limitations of logistic regression?
- Does logistic regression check linear relationships?
- What limits the use of regression analysis?
- Can logistic regression use dummy variables?
- Can logistic regression be used to predict categorical outcome?
- Does scaling affect logistic regression?
- When to use exponentiated coefficient in logistic regression?
- Which is better to categorise a continuous variable or a predictor?
- Which is the correct solution for logistic regression?
- When does overfitting occur in a logistic regression model?

### What type of variables are used in logistic regression?

Logistic regression is the statistical technique used to predict the relationship between predictors (our independent variables) and **a predicted variable (the dependent variable)** where the dependent variable is binary (e.g., sex , response , score , etc…).

### Can regression be used for continuous variables?

Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic **regression** should be used. ... The independent variables used in regression can be either continuous or dichotomous.

### Can you do logistic regression on categorical variables?

Logistic regression is a pretty flexible method. **It can readily use as independent variables categorical variables**. Most software that use Logistic regression should let you use categorical variables.

### Do you need to normalize variables for logistic regression?

**Standardization isn't required for logistic regression**. The main goal of standardizing features is to help convergence of the technique used for optimization. For example, if you use Newton-Raphson to maximize the likelihood, standardizing the features makes the convergence faster.

### What are the limitations of logistic regression?

The major limitation of Logistic Regression is **the assumption of linearity between the dependent variable and the independent variables**. It not only provides a measure of how appropriate a predictor(coefficient size)is, but also its direction of association (positive or negative).

### Does logistic regression check linear relationships?

Answer: First, **logistic regression does not require a linear relationship betweenthe dependent and independent variables**. ... Finally, the dependent variable in logistic regression is not measured on an interval or ratio scale.

### What limits the use of regression analysis?

Despite the above utilities and usefulness, the technique of regression analysis suffers form the following serious limitations: ... It **involves very lengthy and complicated procedure of calculations and analysis**. It cannot be used in case of qualitative phenomenon viz. honesty, crime etc.

### Can logistic regression use dummy variables?

In logistic regression models, **encoding all of the independent variables** as dummy variables allows easy interpretation and calculation of the odds ratios, and increases the stability and significance of the coefficients.

### Can logistic regression be used to predict categorical outcome?

Logistic Regression is a classification algorithm which is used when we want to predict a **categorical variable** (Yes/No, Pass/Fail) based on a set of independent variable(s). In the Logistic Regression model, the log of odds of the dependent variable is modeled as a linear combination of the independent variables.

### Does scaling affect logistic regression?

The **performance of logistic regression did not improve with data scaling**. ... The reason is that, if there predictor variables with large ranges that do not effect the target variable, a regression algorithm will make the corresponding coefficients ai small so that they do not effect predictions so much.

### When to use exponentiated coefficient in logistic regression?

- $\\begingroup$If
**you**include a**continuous**predictor**in**your**logistic regression**, the exponentiated coefficient represents the odds ratio for one unit change**in**the predictor. Often, one unit isn't meaningful and**you**want the odds ratio for, say, 10 units.

### Which is better to categorise a continuous variable or a predictor?

- Given a choice between a
**continuous**variable as a predictor and categorising a**continuous**variable for predictors, the first is usually to be preferred. At the crudest level,**you**are just throwing away information by categorising a**continuous**variable. There is discussion**in**several places.

### Which is the correct solution for logistic regression?

- Correct Analysis (or one proper solution amongst others:
**Logistic regression**model ) where / (\\pi_i/)=P (survival=1) probability of survival for person i, and X1i is age of a person i. Below is the donner.sas SAS program. It reads a datafile stored on your computer.

### When does overfitting occur in a logistic regression model?

- Overfitting tends to happen when
**you**have few observations which**you**are trying to predict with a large number of parameters. This is what**you**are doing**in**your Model 2 since**you**have 8 observations which**you**are trying to explain with 7 parameters.