# INTERCEPT Function in Excel

### Part 1: Introduce

#### Definition

The INTERCEPT function in Excel calculates the intercept of the linear regression line through a set of data points.

#### Purpose

The function is used to predict the value of the dependent variable when the independent variable is zero. It’s often used in statistical analysis and forecasting in various fields, including business and economics.

#### Syntax & Arguments

syntax
```INTERCEPT(known_y's, known_x's) ```
• known_y’s: Required. An array or cell range of numeric dependent data points.
• known_x’s: Required. The set of independent data points.

#### Explain the Arguments in the Function

• known_y’s: These are the observed values of the variable you are trying to predict or explain.
• known_x’s: These are the experimental values of the variable you use to predict the known_y’s.

#### Return Value

The INTERCEPT function returns the intercept of the linear regression line.

#### Remarks

• The arguments must be either numbers or names, arrays, or references that contain numbers.
• If the known_y’s and known_x’s are empty or have a different number of data points, INTERCEPT returns the #N/A error value.

### Part 2: Examples

#### Example 1

• Purpose of Example: To calculate the intercept for sales prediction based on advertising spend.
• Data Tables and Formulas:
ABC
2100500=INTERCEPT(B2:B4, A2:A4)
32001000100
43001500
• Explanation: The intercept value of 100 means that the predicted sales would be 100 units if there is no advertising spend.

#### Example 2

• Purpose of Example: To calculate the intercept for predicting profit based on the number of employees.
• Data Tables and Formulas:
ABC
1EmployeesProfitIntercept
2101000=INTERCEPT(B2:B4, A2:A4)
3202000500
4303000
• Explanation: The intercept value of 500 means that the predicted profit would be 500 currency units with zero employees.

#### Example 3

• Purpose of Example: To calculate the intercept for predicting revenue based on the number of units sold.
• Data Tables and Formulas:
ABC
1Units SoldRevenueIntercept
2505000=INTERCEPT(B2:B4, A2:A4)
3100100001000
415015000
• Explanation: The intercept value of 1000 means that with zero units sold, the predicted revenue would be 1000 currency units.

#### Example 4

• Purpose of Example: To calculate the intercept for predicting customer satisfaction based on the number of support staff.
• Data Tables and Formulas:
ABC
1Support StaffSatisfactionIntercept
2570=INTERCEPT(B2:B4, A2:A4)
3108060
41590
• Explanation: The intercept value of 60 means that the predicted customer satisfaction score would be 60 with zero support staff.

#### Example 5

• Purpose of Example: To calculate the intercept for predicting website traffic based on the number of blog posts.
• Data Tables and Formulas:
ABC
1Blog PostsTrafficIntercept
2101000=INTERCEPT(B2:B4, A2:A4)
3202000500
4303000
• Explanation: The intercept value of 500 means that with zero blog posts, the predicted website traffic would be 500 visitors.

#### Example 6

• Purpose of Example: To calculate the intercept for sales prediction based on advertising spend only if the advertising spend is above a certain amount.
• Data Tables and Formulas:
ABCD
2100500=IF(A2>150, INTERCEPT(B2:B4, A2:A4), “N/A”)N/A
32001000=IF(A3>150, INTERCEPT(B2:B4, A2:A4), “N/A”)100
43001500=IF(A4>150, INTERCEPT(B2:B4, A2:A4), “N/A”)100
• Explanation: The intercept value 100 is calculated only for rows where the advertising spend is above 150. For the first row, where the advertising spend is 100, the function returns “N/A”.

#### Example 7

• Purpose of Example: To calculate the sum of the intercepts for predicting profit based on the number of employees.
• Data Tables and Formulas:
ABCD
1EmployeesProfitInterceptSum
2101000=INTERCEPT(B2:B4, A2:A4)=SUM(C2:C4)
3202000=INTERCEPT(B2:B4, A2:A4)1500
4303000=INTERCEPT(B2:B4, A2:A4)1500
• Explanation: The SUM function calculates the total intercepts calculated in column C. The total intercept is 1500.

#### Example 8

• Purpose of Example: To calculate the intercept for predicting revenue based on the number of units sold and look up the corresponding product name.
• Data Tables and Formulas:
ABCDE
1Units SoldRevenueProduct IDInterceptProduct Name
25050001=INTERCEPT(B2:B4, A2:A4)=VLOOKUP(D2, G2:H4, 2, FALSE)
3100100002=INTERCEPT(B2:B4, A2:A4)=VLOOKUP(D3, G2:H4, 2, FALSE)
4150150003=INTERCEPT(B2:B4, A2:A4)=VLOOKUP(D4, G2:H4, 2, FALSE)
GH
1Product IDProduct Name
21Product A
32Product B
43Product C
• Explanation: The VLOOKUP function is used to find the product name corresponding to the product ID in column D. The intercept is calculated for each row in column C.

#### Example 9

• Purpose of Example: To calculate the intercept for predicting customer satisfaction based on the number of support staff and round the result to the nearest integer.
• Data Tables and Formulas:
ABCD
1Support StaffSatisfactionInterceptRounded Intercept
2570=INTERCEPT(B2:B4, A2:A4)=ROUND(C2, 0)
31080=INTERCEPT(B2:B4, A2:A4)=ROUND(C3, 0)
41590=INTERCEPT(B2:B4, A2:A4)=ROUND(C4, 0)
• Explanation: The ROUND function rounds the intercept to the nearest integer. This could be useful in scenarios where the intercept must be a whole number, such as predicting the number of items sold or people attending an event.

#### Example 10

• Purpose of Example: To calculate the intercept for predicting website traffic based on the number of blog posts and display a custom message if the intercept is above a certain threshold.
• Data Tables and Formulas:
ABCD
1Blog PostsTrafficInterceptMessage
2101000=INTERCEPT(B2:B4, A2:A4)=IF(C2>500, “High Intercept”, “Low Intercept”)
3202000=INTERCEPT(B2:B4, A2:A4)=IF(C3>500, “High Intercept”, “Low Intercept”)
4303000=INTERCEPT(B2:B4, A2:A4)=IF(C4>500, “High Intercept”, “Low Intercept”)
• Explanation: The IF function displays a custom message based on the value of the intercept. If the intercept is above 500, the letter “High Intercept” is displayed. Otherwise, the message “Low Intercept” is displayed.

#### Example 11

• Purpose of Example: To calculate the intercept for predicting sales based on the number of salespeople and calculate the average of these intercepts.
• Data Tables and Formulas:
ABCD
1SalespeopleSalesInterceptAverage Intercept
25500=INTERCEPT(B2:B4, A2:A4)=AVERAGE(C2:C4)
3101000=INTERCEPT(B2:B4, A2:A4)=AVERAGE(C2:C4)
4151500=INTERCEPT(B2:B4, A2:A4)=AVERAGE(C2:C4)
• Explanation: The AVERAGE function calculates the average intercepts calculated in column C. This could be useful in scenarios where you want to understand the average effect of the independent variable on the dependent variable across different data sets.

#### Example 12

• Purpose of Example: To calculate the intercept for predicting revenue based on the number of units sold and find the maximum of these intercepts.
• Data Tables and Formulas:
ABCD
1Units SoldRevenueInterceptMax Intercept
2505000=INTERCEPT(B2:B4, A2:A4)=MAX(C2:C4)
310010000=INTERCEPT(B2:B4, A2:A4)=MAX(C2:C4)
415015000=INTERCEPT(B2:B4, A2:A4)=MAX(C2:C4)
• Explanation: The MAX function finds the maximum intercept calculated in column C. This could be useful in scenarios where you want to understand the maximum effect of the independent variable on the dependent variable across different data sets.

#### Example 13

• Purpose of Example: To calculate the intercept for predicting customer satisfaction based on the number of support staff and find the minimum of these intercepts.
• Data Tables and Formulas:
ABCD
1Support StaffSatisfactionInterceptMin Intercept
2570=INTERCEPT(B2:B4, A2:A4)=MIN(C2:C4)
31080=INTERCEPT(B2:B4, A2:A4)=MIN(C2:C4)
41590=INTERCEPT(B2:B4, A2:A4)=MIN(C2:C4)
• Explanation: The MIN function finds the minimum intercept calculated in column C. This could be useful in scenarios where you want to understand the minimum effect of the independent variable on the dependent variable across different data sets.

### Part 3: Tips and Tricks

1. Check for Errors: If the INTERCEPT function returns an error, check if the known_y’s and known_x’s arrays have the same data points and contain numbers, not text or logical values.
2. Use with Other Functions: The INTERCEPT function can be used with Excel functions like SLOPE and LINEST to perform more complex statistical analyses.
3. Interpret with Caution: The intercept has a meaningful interpretation only if zero is a sensible value of the independent variable within the scope of the problem.
4. Data Scaling: If your data spans several orders of magnitude, consider scaling the data before using the INTERCEPT function to avoid numerical errors.
5. Avoid Extrapolation: Be cautious when using the INTERCEPT function for extrapolation. The predictions are most reliable within the range of the known_x’s.