# The Durbin-Watson Test in R: How to Analyze and Interpret Your Results

## Durbin-Watson (DW) test

- Durbin-Watson (DW) test is used for analyzing the first-order autocorrelation (also known as serial correlation) in ordinary least square (OLS) regression analysis in time series dataset i.e. residuals are independent of one time to its prior time. First-order autocorrelation is a correlation between the successive (present and prior) residuals for the same variable.
- Durbin-Watson test is commonly used on time series dataset (e.g. events measured on different periods) as time series dataset tends to exhibit positive autocorrelation.
- The value of autocorrelation can range from -1 to 1, where -1 to 0 range represents negative autocorrelation whereas the range 0 to 1 represents positive autocorrelation.

## Durbin-Watson test Hypotheses and statistics

Durbin-Watson test analyzes the **null hypothesis** that residuals from the regression are not autocorrelated
(autocorrelation coefficient, ρ = 0) versus the **alternative hypothesis** that residuals from the regression are
positively autocorrelated (autocorrelation coefficient, ρ > 0)

Durbin-Watson test statistics *d* is given as,

Durbin-Watson test statistics (*d*) ranges from 0 to 4 and see following table for Durbin-Watson test statistic
interpretation,

d |
Interpretation |
---|---|

d = 2 |
No autocorrelation (ρ = 0) |

d < 2 (d = 0) |
positive autocorrelation (perfect positive autocorrelation i.e. ρ = +1) |

d > 2 (d = 4 ) |
negative autocorrelation (perfect negative autocorrelation i.e. ρ = -1) |

In practice, if the *d* is in between 1.5 and 2.5, it indicates there is no autocorrelation. If *d* < 1.5, it indicates
positive autocorrelation, whereas if *d* > 2.5, it indicates negative autocorrelation.

The Durbin-Watson test *p* value depends on the Durbin-Watson statistic (*d*) value, the number of independent variables
(*k*) in the regression, and the total number of observations (*N*).

In terms of Durbin-Watson critical values table, if
*d* < lower critical value (*d*_{L}), reject the null hypothesis, whereas if *d* > upper critical value
(*d*_{U}), fail to reject null hypothesis. The region between *d*_{L} and *d*_{U} is inconclusive.

If *d* < *d*_{L}, indicates a significantly small *p* value (say *p* < 0.05) and implies substantial positive
autocorrelation within the residuals. In contrast, if *d* > *d*_{U} then *p* value is larger (say *p* > 0.05),
we fail to reject the null hypothesis and conclude that residuals are not autocorrelated. If *d* is substantially larger than 2,
you should also test the hypothesis for negative autocorrelation.

Learn more about hypothesis testing and interpretation

## Perform Durbin-Watson test in R

We will use the `tidyverse`

, `stats`

, and `lmtest`

R packages for this tutorial.

### Dataset

Suppose, we have a hypothetical time series dataset consisting of company stock price over a period of 12 months. You can
import your dataset using `read_csv()`

function available in tidyverse package.

```
# R version 4.1.2 (2021-11-01)
library(tidyverse)
df = read_csv("https://reneshbedre.github.io/assets/posts/reg/stock_price.csv")
head(df, 2)
# output
months stock_price
<dbl> <dbl>
1 1 122
2 2 129
```

Check out other ways to import CSV datasets in R

### Fit the regression model

Fit the regression model with `months`

as independent variables and `stock_price`

as the dependent variable,

```
library(stats)
model <- lm(formula = stock_price ~ months, data = df)
model
# output
Call:
lm(formula = stock_price ~ months, data = df)
Coefficients:
(Intercept) months
114.61 5.92
```

Learn more about regression analysis

### Calculate Durbin-Watson (DW) test in R

We will use `dwtest()`

function avialble in `lmtest`

R package for performing the Durbin-Watson (DW) test. The `dwtest()`

function takes the fitted regression model and returns DW test statistics (*d*) and *p* value.

```
library(lmtest)
dwtest(formula = model, alternative = "two.sided")
# output
Durbin-Watson test
data: model
DW = 2.5848, p-value = 0.4705
alternative hypothesis: true autocorrelation is not 0
```

In the Durbin-Watson critical values table, the critical region lies between 0.97 (dL) and 1.33 (dU) for N=12 at 5% significance. Since the Durbin-Watson test statistic (DW=2.58) is higher than 1.33 (DW > dU), we fail to reject the null hypothesis (p > 0.05) that there is no autocorrelation.

As the *p* value obtained from the Durbin-Watson test
is not significant (*d* = 2.584, *p* = 0.470), we fail to reject the null hypothesis. Hence, we conclude that the residuals
are not autocorrelated.

Check how to perform Durbin-Watson in Python

## Enhance your skills with statistical courses using R

- Introduction to Statistics
- Understanding Clinical Research: Behind the Statistics
- Statistics with R Specialization
- Data Science: Foundations using R Specialization

## References

- Salamon SJ, Hansen HJ, Abbott D. How real are observed trends in small correlated datasets?. Royal Society open science. 2019 Mar 20;6(3):181089.
- Turner SL, Forbes AB, Karahalios A, Taljaard M, McKenzie JE. Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study. BMC medical research methodology. 2021 Dec;21(1):1-8.
- Durbin-Watson

If you have any questions, comments, corrections, or recommendations, please email me at
**reneshbe@gmail.com**

This work is licensed under a Creative Commons Attribution 4.0 International License

Some of the links on this page may be affiliate links, which means we may get an affiliate commission on a valid purchase. The retailer will pay the commission at no additional cost to you.