# When to Use Linear Regression: A Guide for Applying This Versatile Statistical Technique

1. Methods in Epidemiologic, Clinical, and Operations Research-MECOR-program, American Thoracic Society/Asociación Latinoamericana del Tórax, Montevideo, Uruguay.

1. Methods in Epidemiologic, Clinical, and Operations Research-MECOR-program, American Thoracic Society/Asociación Latinoamericana del Tórax, Montevideo, Uruguay.

3. Divisão de Pneumologia, Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo (SP) Brasil.Find articles by

A secondary analysis 1 of a study designated “Integrating Palliative and Critical Care,” a cluster randomized trial, was conducted to explore differences in receipt of elements of palliative care among patients who died in the ICU with interstitial lung disease (ILD) or COPD in comparison with those who died of cancer. The authors used two methods of multiple regression analysis: linear regression to estimate the impact of COPD and ILD, in comparison with that of cancer, on the length of ICU stay, and logistic regression to evaluate the effects of COPD and ILD on the presence or absence of elements of palliative care. All regression models were adjusted for confounders (age, sex, minority status, education level, among others) of the association between the patient diagnosis and palliative care outcomes.

Linear and logistic regressions are widely used statistical methods to assess the association between variables in medical research. These methods estimate if there is an association between the independent variable (also called predictor, exposure, or risk factor) and the dependent variable (outcome). 2

The association between two variables is evaluated with simple regression analysis. However, in many clinical scenarios, more than one independent variable may be associated with the outcome, and there may be the need to control for confounder variables. When more than two independent variables are associated with the outcome, multiple regression analysis is used. Multiple regression analysis evaluates the independent effect of each variable on the outcome, adjusting for the effect of the other variables included in the same regression model.

As someone interested in analyzing data and models, you may have heard about linear regression. It’s one of the most common and powerful techniques in statistics. But when exactly should you use linear regression in your own data analysis? In this article, I’ll provide a beginner’s guide to knowing when linear regression is the right tool for your needs

## What is Linear Regression?

Let’s start with a quick refresher on what linear regression is Linear regression is used to model and analyze the relationship between two or more variables It does this by fitting a linear equation to the data set.

One variable is considered the predictor or independent variable The other is the response or dependent variable that you are trying to predict. Linear regression finds the line of best fit through the data points This line can be used to predict future values of the response variable based on values of the predictor variables.

For example, linear regression could relate sales revenue (response variable) to advertising spend (predictor variable). The fitted line would then allow you to predict revenue for a given value of advertising spend.

Linear regression is especially useful when you want to:

• Quantify the strength of the relationship between predictor and response variables.

• Make predictions about a response using one or more predictors.

• Determine which predictors matter most in affecting a response variable.

## When Should You Use Linear Regression?

Now that you know what linear regression does, when should you use it for your own data analysis? Here are four key things to look for:

### 1. Continuous Response Variable

Linear regression works best when your response variable is measured on a continuous scale. For example, a response can be a person’s height, blood pressure, salary, or any other number. Linear regression is less effective when predicting categorical responses like “yes/no”, “disease A/disease B”, etc.

### 2. Linear Relationship

As the name implies, linear regression assumes the relationship between the predictors and response is approximately linear. The fit line should follow the general trend of the data points. If your data shows a strongly curved or non-linear pattern, linear regression is not the best method.

You can check for linearity by plotting the variables on a scatterplot. Or by analyzing residual plots after running the regression. A linear trend will show no systematic pattern in the residuals.

### 3. No Perfect Multicollinearity

Multicollinearity refers to high correlations between two or more predictor variables. Perfect multicollinearity makes the regression coefficients unstable and difficult to interpret. Before running linear regression, check predictors for correlations above 0.80 or 0.90. Consider dropping or combining highly correlated variables.

### 4. Data Meeting Assumptions

Linear regression relies on a few key assumptions. The data should have normality, homoscedasticity (constant variance), and independence of observations. You can test these assumptions using residual plots, hypothesis tests, and other methods. Be cautious applying linear regression to data that does not satisfy the assumptions. Transformations like log or sqrt can sometimes help normalize skewed data.

## Examples of When to Use Linear Regression

To make things more concrete, here are some examples of appropriate uses of linear regression:

• Relating crop yield (response) to fertilizer amount (predictor). The response is continuous, the relationship is approximately linear, and no perfect multicollinearity.

• Predicting product sales numbers (response) from ad spending, price, promotions, etc. (predictors). Continuous response and multiple potential linear effects.

• Modeling exam score (response) based on time studied (predictor). Continuous variable and likely linear relationship.

• Estimating life expectancy (response) from weight, diet, exercise, and other health factors (predictors). Continuous response with multiple linear predictors.

• Relating distance traveled to amount of gas consumed. Continuous variables with suspected linear relationship.

In all these cases, linear regression would be an appropriate analysis technique. The key is having a continuous response, avoiding perfect multicollinearity, and meeting the linear regression assumptions.

## When Not to Use Linear Regression

Since we’ve covered times when linear regression is appropriate, let’s also discuss situations where it is not the best approach:

• Categorical response variable. Logistic or probit regression are better for binary responses like pass/fail.

• Highly nonlinear data. Consider nonlinear regression, general additive models, or data transformations.

-perfect multicollinearity. Remove redundant variables or combine them into one predictor.

• Time series data. ARIMA or other time series models account for temporal structure.

• Survival analysis with censoring. Use survival models like Cox regression which handle censored data.

• Count or rate data. Poisson and negative binomial regression suit count variables like number of accidents.

• Highly influential outliers. Robust regression lessens the effect of outliers on the model.

• Data not meeting assumptions. Try transformations, weighted least squares, generalized linear models or nonparametric regression depending on the violation.

The key is identifying aspects of the data that violate the premises of linear regression. Then choosing alternative methods designed to handle those issues.

## A Flexible Data Analysis Tool

Used properly, linear regression is one of the most powerful and flexible tools for modeling data. Its versatility and easy interpretation has made it popular across industries like social science, biology, finance, and more. By understanding when linear regression aligns with your data and goals, you’ll be able to apply this tool effectively in your own analyses.

Some good rules of thumb:

• Ensure a continuous, unbounded response variable

• Check for an approximate linear relationship

• Watch for high correlations between predictors

• Test assumptions and transform variables if needed

Following these guidelines will help you maximize the potential of linear regression while avoiding pitfalls from applying it inappropriately. With a solid handle on linear regression, you’ll be equipped to tackle a wide array of real-world data modeling problems.

## WHEN TO USE LINEAR OR LOGISTIC REGRESSION?

The determinant of the type of regression analysis to be used is the nature of the outcome variable. Linear regression is used for continuous outcome variables (e.g., days of hospitalization or FEV1), and logistic regression is used for categorical outcome variables, such as death. Independent variables can be continuous, categorical, or a mix of both.

In our example, the authors wanted to know if there was a relationship between cancer, COPD, and ILD (baseline disease; the independent variables) with two different outcomes. One outcome was continuous (length of ICU stay) and the other one was categorical (presence or absence of elements of palliative care). Therefore, two models were built: a linear model to examine the association between baseline disease (chronic pulmonary disease or cancer) and length of ICU stay, and a logistic regression analysis to examine the association between the baseline disease and being in receipt of elements of palliative care.

## Linear Regression in 2 minutes

When should you use simple linear regression?

You can use simple linear regression when you want to know: How strong the relationship is between two variables (e.g., the relationship between rainfall and soil erosion). The value of the dependent variable at a certain value of the independent variable (e.g., the amount of soil erosion at a certain level of rainfall).

What is linear regression in statistics?

More formally, linear regression is a statistical technique for modeling the linear relationship between a dependent variable y and one or more independent variables x. The dependent variable is also called the outcome or response variable. The independent variables are also called explanatory or predictor variables.

Why should you use linear regression analysis software?

When solving linear regression, it’s important to use these types of visuals to help you locate the values you need to complete calculations for evaluating different business metrics. If you plan to use linear regression regularly, regression analysis software can streamline this process. Read more: Linear Regression: A Definitive Guide

Are linear regression models easy to interpret?

Linear regression models are known for being easy to interpret thanks to the applications of the model equation, both for understanding the underlying relationship and in applying the model to predictions. The fact that regression analysis is great for explanatory analysis and often good enough for prediction is rare among modeling techniques.