Microsoft Excel has a few statistical functions that can help you to do linear regression

analysis such as LINEST, SLOPE, INTERCPET, and CORREL

The LINEST function uses the least squares regression method to calculate a straight line

that best explains the relationship between your variables and returns an array describing

that line. You can find the detailed explanation of the function's syntax in this tutorial.

For now, let's just make a formula for our sample dataset:

=LINEST(C2:C25, B2:B25)

Because the LINEST function returns an array of values, you must enter it as an array formula. Select two adjacent cells in the same row, E2:F2 in our case, type the formula,

and press Ctrl + Shift + Enter to complete it.

The formula returns the b coefficient (E1) and the a constant (F1) for the already familiar

linear regression equation:

y = bx + a

If you avoid using array formulas in your worksheets, you can calculate a and b

individually with regular formulas:

Get the Y-intercept (a):

=INTERCEPT(C2:C25, B2:B25)

Get the slope (b):

=SLOPE(C2:C25, B2:B25)

Additionally, you can find the **correlation coefficient **(Multiple R in the regression

analysis summary output) that indicates how strongly the two variables are related to

each other:

=CORREL(B2:B25,C2:C25)

The following screenshot shows all these Excel regression formulas in action:

**Tip**. If you'd like to get additional statistics for your regression analysis, use the LINEST

function with the stats parameter set to TRUE as shown in this example.

That's how you do linear regression in Excel. That said, please keep in mind that

Microsoft Excel is not a statistical program. If you need to perform regression analysis at

the professional level, you may want to use targeted software such as XLSTAT, RegressIt, etc

1- Types of data and level of measurement
2- Graphs and Tables that Represent Categorical Variables
3- Excel formulas
4- Graphs and tables that represent numerical variables
5- Graphs and Tables for Relationships Between Variables.
6- Mean, Median, Mode
7- Variance and Standard Deviation
8- Covariance and Correlation
9- Distributions
10- The Central Limit Theorem
11- Estimators and Estimates
12- Confidence Intervals and the Margin of Error
13- Studentâ€™s T Distribution
14- Formulas for Confidence Intervals
15- Scientific method
16- Hypotheses
17- Decisions You Can Take
18- Statistical Errors (Type I Error and Type II Error)
19- P-Value
20- Formulae for Hypothesis Testing
21- Basics
22- Linear regression equation
23- How to do linear regression in Excel with Analysis ToolPak
24- Interpret regression analysis output
25- How to make a linear regression graph in Excel
26- How to do regression in Excel using formulas