In statistics, linear regression models are used to quantify the relationship between one or more predictor variables and a response variable.

Whenever you perform regression analysis using some statistical software, you will receive a regression table that summarizes the results of the model.

Two of the most important values in a regression table are the regression coefficients and their corresponding **p-values**.

The p-values tell you whether or not there is a statistically significant relationship between each predictor variable and the response variable.

The following example shows how to interpret the p-values of a multiple linear regression model in practice.

**Example: Interpreting P-Values in Regression Model**

Suppose we want to fit a regression model using the following variables:

**Predictor Variables**

- Total number of hours studied (between 0 and 20)
- Whether or not a student used a tutor (yes or no)

**Response Variable**

- Exam score ( between 0 and 100)

We want to examine the relationship between the predictor variables and the response variable to find out if hours studied and tutoring actually have a meaningful impact on exam score.

Suppose we run a regression analysis and get the following output:

Term | Coefficient | Standard Error | t Stat | P-value |
---|---|---|---|---|

Intercept |
48.56 | 14.32 | 3.39 | 0.002 |

Hours studied |
2.03 | 0.67 | 3.03 | 0.009 |

Tutor |
8.34 | 5.68 | 1.47 | 0.138 |

Here’s how to interpret the output for each term in the model:

**Interpreting the P-value for Intercept**

The **intercept **term in a regression table tells us the average expected value for the response variable when all of the predictor variables are equal to zero.

In this example, the regression coefficient for the intercept is equal to **48.56**. This means that for a student who studied for zero hours*, *the average expected exam score is 48.56.

The p-value is **0.002**, which tells us that the intercept term is statistically different than zero.

In practice, we don’t usually care about the p-value for the intercept term. Even if the p-value isn’t less than some significance level (e.g. 0.05), we would still keep the intercept term in the model.

**Interpreting the P-value for a Continuous Predictor Variable**

In this example, **Hours studied **is a continuous predictor variable that ranges from 0 to 20 hours.

From the regression output, we can see that the regression coefficient for Hours studied is **2.03**. This means that, on average, each additional hour studied is associated with an increase of 2.03 points on the final exam, assuming the predictor variable **Tutor **is held constant.

For example, consider student A who studies for 10 hours and uses a tutor. Also consider student B who studies for 11 hours and also uses a tutor. According to our regression output, student B is expected to receive an exam score that is **2.03** points higher than student A.

The corresponding p-value is **0.009**, which is statistically significant at an alpha level of 0.05.

This tells us that that the average change in exam score for each additional hour studied is **statistically significantly different than zero**.

Another way to put this: **Hours studied** has a statistically significant relationship with the response variable **exam score**.

**Interpreting the P-value for a Categorical Predictor Variable**

In this example, **Tutor **is a categorical predictor variable that can take on two different values:

- 1 = the student used a tutor to prepare for the exam
- 0 = the student did not used a tutor to prepare for the exam

From the regression output, we can see that the regression coefficient for Tutor is **8.34**. This means that, on average, a student who used a tutor scored 8.34 points higher on the exam compared to a student who did not used a tutor, assuming the predictor variable Hours studied is held constant.

For example, consider student A who studies for 10 hours and uses a tutor. Also consider student B who studies for 10 hours and does not use a tutor. According to our regression output, student A is expected to receive an exam score that is 8.34 points higher than student B.

The corresponding p-value is **0.138**, which is not statistically significant at an alpha level of 0.05.

This tells us that that the average change in exam score for each additional hour studied is **not statistically significantly different than zero**.

Another way to put this: The predictor variable **Tutor** does not have a statistically significant relationship with the response variable **exam score**.

This indicates that although students who used a tutor scored higher on the exam, this difference could have been due to random chance.

**Additional Resources**

The following tutorials provide additional information about linear regression:

How to Interpret the F-Test of Overall Significance in Regression

The Five Assumptions of Multiple Linear Regression

Understanding the t-Test in Linear Regression