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Multiple Linear Regression | A Quick Guide (Examples)

Published on February 20, 2020 by Rebecca Bevans . Revised on June 22, 2023.

Regression models are used to describe relationships between variables by fitting a line to the observed data. Regression allows you to estimate how a dependent variable changes as the independent variable(s) change.

Multiple linear regression is used to estimate the relationship between  two or more independent variables and one dependent variable . You can use multiple linear regression when you want to know:

  • How strong the relationship is between two or more independent variables and one dependent variable (e.g. how rainfall, temperature, and amount of fertilizer added affect crop growth).
  • The value of the dependent variable at a certain value of the independent variables (e.g. the expected yield of a crop at certain levels of rainfall, temperature, and fertilizer addition).

Table of contents

Assumptions of multiple linear regression, how to perform a multiple linear regression, interpreting the results, presenting the results, other interesting articles, frequently asked questions about multiple linear regression.

Multiple linear regression makes all of the same assumptions as simple linear regression :

Homogeneity of variance (homoscedasticity) : the size of the error in our prediction doesn’t change significantly across the values of the independent variable.

Independence of observations : the observations in the dataset were collected using statistically valid sampling methods , and there are no hidden relationships among variables.

In multiple linear regression, it is possible that some of the independent variables are actually correlated with one another, so it is important to check these before developing the regression model. If two independent variables are too highly correlated (r2 > ~0.6), then only one of them should be used in the regression model.

Normality : The data follows a normal distribution .

Linearity : the line of best fit through the data points is a straight line, rather than a curve or some sort of grouping factor.

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Multiple linear regression formula

The formula for a multiple linear regression is:

y = {\beta_0} + {\beta_1{X_1}} + … + {{\beta_n{X_n}} + {\epsilon}

  • … = do the same for however many independent variables you are testing

B_nX_n

To find the best-fit line for each independent variable, multiple linear regression calculates three things:

  • The regression coefficients that lead to the smallest overall model error.
  • The t statistic of the overall model.
  • The associated p value (how likely it is that the t statistic would have occurred by chance if the null hypothesis of no relationship between the independent and dependent variables was true).

It then calculates the t statistic and p value for each regression coefficient in the model.

Multiple linear regression in R

While it is possible to do multiple linear regression by hand, it is much more commonly done via statistical software. We are going to use R for our examples because it is free, powerful, and widely available. Download the sample dataset to try it yourself.

Dataset for multiple linear regression (.csv)

Load the heart.data dataset into your R environment and run the following code:

This code takes the data set heart.data and calculates the effect that the independent variables biking and smoking have on the dependent variable heart disease using the equation for the linear model: lm() .

Learn more by following the full step-by-step guide to linear regression in R .

To view the results of the model, you can use the summary() function:

This function takes the most important parameters from the linear model and puts them into a table that looks like this:

R multiple linear regression summary output

The summary first prints out the formula (‘Call’), then the model residuals (‘Residuals’). If the residuals are roughly centered around zero and with similar spread on either side, as these do ( median 0.03, and min and max around -2 and 2) then the model probably fits the assumption of heteroscedasticity.

Next are the regression coefficients of the model (‘Coefficients’). Row 1 of the coefficients table is labeled (Intercept) – this is the y-intercept of the regression equation. It’s helpful to know the estimated intercept in order to plug it into the regression equation and predict values of the dependent variable:

The most important things to note in this output table are the next two tables – the estimates for the independent variables.

The Estimate column is the estimated effect , also called the regression coefficient or r 2 value. The estimates in the table tell us that for every one percent increase in biking to work there is an associated 0.2 percent decrease in heart disease, and that for every one percent increase in smoking there is an associated .17 percent increase in heart disease.

The Std.error column displays the standard error of the estimate. This number shows how much variation there is around the estimates of the regression coefficient.

The t value column displays the test statistic . Unless otherwise specified, the test statistic used in linear regression is the t value from a two-sided t test . The larger the test statistic, the less likely it is that the results occurred by chance.

The Pr( > | t | ) column shows the p value . This shows how likely the calculated t value would have occurred by chance if the null hypothesis of no effect of the parameter were true.

Because these values are so low ( p < 0.001 in both cases), we can reject the null hypothesis and conclude that both biking to work and smoking both likely influence rates of heart disease.

When reporting your results, include the estimated effect (i.e. the regression coefficient), the standard error of the estimate, and the p value. You should also interpret your numbers to make it clear to your readers what the regression coefficient means.

Visualizing the results in a graph

It can also be helpful to include a graph with your results. Multiple linear regression is somewhat more complicated than simple linear regression, because there are more parameters than will fit on a two-dimensional plot.

However, there are ways to display your results that include the effects of multiple independent variables on the dependent variable, even though only one independent variable can actually be plotted on the x-axis.

Multiple regression in R graph

Here, we have calculated the predicted values of the dependent variable (heart disease) across the full range of observed values for the percentage of people biking to work.

To include the effect of smoking on the independent variable, we calculated these predicted values while holding smoking constant at the minimum, mean , and maximum observed rates of smoking.

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multiple linear regression hypothesis example

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square test of independence
  • Statistical power
  • Descriptive statistics
  • Degrees of freedom
  • Pearson correlation
  • Null hypothesis

Methodology

  • Double-blind study
  • Case-control study
  • Research ethics
  • Data collection
  • Hypothesis testing
  • Structured interviews

Research bias

  • Hawthorne effect
  • Unconscious bias
  • Recall bias
  • Halo effect
  • Self-serving bias
  • Information bias

A regression model is a statistical model that estimates the relationship between one dependent variable and one or more independent variables using a line (or a plane in the case of two or more independent variables).

A regression model can be used when the dependent variable is quantitative, except in the case of logistic regression, where the dependent variable is binary.

Multiple linear regression is a regression model that estimates the relationship between a quantitative dependent variable and two or more independent variables using a straight line.

Linear regression most often uses mean-square error (MSE) to calculate the error of the model. MSE is calculated by:

  • measuring the distance of the observed y-values from the predicted y-values at each value of x;
  • squaring each of these distances;
  • calculating the mean of each of the squared distances.

Linear regression fits a line to the data by finding the regression coefficient that results in the smallest MSE.

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Lesson 5: Multiple Linear Regression (MLR) Model & Evaluation

Overview of this lesson.

In this lesson, we make our first (and last?!) major jump in the course. We move from the simple linear regression model with one predictor to the multiple linear regression model with two or more predictors. That is, we use the adjective "simple" to denote that our model has only predictor, and we use the adjective "multiple" to indicate that our model has at least two predictors.

In the multiple regression setting, because of the potentially large number of predictors, it is more efficient to use matrices to define the regression model and the subsequent analyses. This lesson considers some of the more important multiple regression formulas in matrix form. If you're unsure about any of this, it may be a good time to take a look at this Matrix Algebra Review .

The good news is that everything you learned about the simple linear regression model extends — with at most minor modification — to the multiple linear regression model. Think about it — you don't have to forget all of that good stuff you learned! In particular:

  • The models have similar "LINE" assumptions. The only real difference is that whereas in simple linear regression we think of the distribution of errors at a fixed value of the single predictor, with multiple linear regression we have to think of the distribution of errors at a fixed set of values for all the predictors. All of the model checking procedures we learned earlier are useful in the multiple linear regression framework, although the process becomes more involved since we now have multiple predictors. We'll explore this issue further in Lesson 6.
  • The use and interpretation of r 2 (which we'll denote R 2 in the context of multiple linear regression) remains the same. However, with multiple linear regression we can also make use of an "adjusted" R 2 value, which is useful for model building purposes. We'll explore this measure further in Lesson 11.
  • With a minor generalization of the degrees of freedom, we use t -tests and t -intervals for the regression slope coefficients to assess whether a predictor is significantly linearly related to the response, after controlling for the effects of all the opther predictors in the model.
  • With a minor generalization of the degrees of freedom, we use confidence intervals for estimating the mean response and prediction intervals for predicting an individual response. We'll explore these further in Lesson 6.

For the simple linear regression model, there is only one slope parameter about which one can perform hypothesis tests. For the multiple linear regression model, there are three different hypothesis tests for slopes that one could conduct. They are:

  • a hypothesis test for testing that one slope parameter is 0
  • a hypothesis test for testing that all of the slope parameters are 0
  • a hypothesis test for testing that a subset — more than one, but not all — of the slope parameters are 0

In this lesson, we also learn how to perform each of the above three hypothesis tests.

  • 5.1 - Example on IQ and Physical Characteristics
  • 5.2 - Example on Underground Air Quality
  • 5.3 - The Multiple Linear Regression Model
  • 5.4 - A Matrix Formulation of the Multiple Regression Model
  • 5.5 - Three Types of MLR Parameter Tests
  • 5.6 - The General Linear F-Test
  • 5.7 - MLR Parameter Tests
  • 5.8 - Partial R-squared
  • 5.9 - Further MLR Examples

Start Here!

  • Welcome to STAT 462!
  • Search Course Materials
  • Lesson 1: Statistical Inference Foundations
  • Lesson 2: Simple Linear Regression (SLR) Model
  • Lesson 3: SLR Evaluation
  • Lesson 4: SLR Assumptions, Estimation & Prediction
  • 5.9- Further MLR Examples
  • Lesson 6: MLR Assumptions, Estimation & Prediction
  • Lesson 7: Transformations & Interactions
  • Lesson 8: Categorical Predictors
  • Lesson 9: Influential Points
  • Lesson 10: Regression Pitfalls
  • Lesson 11: Model Building
  • Lesson 12: Logistic, Poisson & Nonlinear Regression
  • Website for Applied Regression Modeling, 2nd edition
  • Notation Used in this Course
  • R Software Help
  • Minitab Software Help

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