Whenever you perform linear regression in R, the output of your regression model will be displayed in the following format:

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.0035 5.9091 1.693 0.1513 x1 1.4758 0.5029 2.935 0.0325 * x2 -0.7834 0.8014 -0.978 0.3732

The **Pr(>|t|)** column represents the p-value associated with the value in the **t value** column.

If the p-value is less than a certain significance level (e.g. α = .05) then the predictor variable is said to have a statistically significant relationship with the response variable in the model.

The following example shows how to interpret values in the Pr(>|t|) column for a given regression model.

**Example: How to Interpret Pr(>|t|) Values**

Suppose we would like to fit a multiple linear regression model using predictor variables **x1** and **x2** and a single response variable **y**.

The following code shows how to create a data frame and fit a regression model to the data:

**#create data frame
df <- data.frame(x1=c(1, 3, 3, 4, 4, 5, 6, 6),
x2=c(7, 7, 5, 6, 5, 4, 5, 6),
y=c(8, 8, 9, 9, 13, 14, 17, 14))
#fit multiple linear regression model
model <- lm(y ~ x1 + x2, data=df)
#view model summary
summary(model)
Call:
lm(formula = y ~ x1 + x2, data = df)
Residuals:
1 2 3 4 5 6 7 8
2.0046 -0.9470 -1.5138 -2.2062 1.0104 -0.2488 2.0588 -0.1578
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.0035 5.9091 1.693 0.1513
x1 1.4758 0.5029 2.935 0.0325 *
x2 -0.7834 0.8014 -0.978 0.3732
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.867 on 5 degrees of freedom
Multiple R-squared: 0.7876, Adjusted R-squared: 0.7026
F-statistic: 9.268 on 2 and 5 DF, p-value: 0.0208**

Here’s how to interpret the values in the Pr(>|t|) column:

- The p-value for the predictor variable x1 is
**.0325**. Since this value is less than .05, it has a statistically significant relationship with the response variable in the model. - The p-value for the predictor variable x2 is
**.3732**. Since this value is not less than .05, it does not have a statistically significant relationship with the response variable in the model.

The significance codes under the coefficient table tell us that a single asterik (*) next to the p-value of .0325 means the p-value is statistically significant at α = .05.

**How is Pr(>|t|) Actually Calculated?**

Here’s how the value for Pr(>|t|) is actually calculated:

**Step 1: Calculate the t value**

First, we calculate the **t value** using the following formula:

**t value**= Estimate / Std. Error

For example, here’s how to calculate the t value for the predictor variable x1:

#calculate t-value 1.4758 / .5029 [1] 2.934579

**Step 2: Calculate the p-value**

Next, we calculate the p-value. This represents the probability that the absolute value of the t-distribution is greater than 2.935.

We can use the following formula in R to calculate this value:

**p-value**= 2 * pt(abs(t value), residual df, lower.tail = FALSE)

For example, here’s how to calculate the p-value for a t-value of 2.935 with 5 residual degrees of freedom:

**#calculate p-value
2 * pt(abs(2.935), 5, lower.tail = FALSE)
[1] 0.0324441
**

Notice that this p-value matches the p-value in the regression output from above.

**Note:** The value for the residual degrees of freedom can be found near the bottom of the regression output. In our example, it turned out to be 5:

**Residual standard error: 1.867 on 5 degrees of freedom
**

**Additional Resources**

How to Perform Simple Linear Regression in R

How to Perform Multiple Linear Regression in R

How to Plot Multiple Linear Regression Results in R