Linear regression analysis, in general, is a statistical method that shows or predicts the relationship between two variables or factors.
There are 2 types of factors in regression analysis:
- Dependent variable (y): It’s also called the ‘criterion variable’, ‘response’, or ‘outcome’ and is the factor being solved.
- Independent variable (x): This is otherwise known as ‘explanatory variables’ or ‘predictors’. They are factors used in solving the dependent variable due to their influence or effect on the said variable.
Here’s the linear regression formula:
y = bx + a + ε
As you can see, the equation shows how y is related to x.
On an Excel chart, there’s a trendline you can see which illustrates the regression line — the rate of change.
Here’s a more detailed definition of the formula’s parameters:
- y (dependent variable)
- b (the slope of the regression line)
- x (independent variable)
- a (y-intercept of the regression line)
- ε (the error term which accounts the variability in y that can’t be explained by the analysis)
The analysis accounts for an error since they can’t be completely eliminated especially in a predictive analysis such as this.
But don’t be surprised if you can’t find the error term in Excel. The program does it in the background.
Now, let’s proceed into making one in Excel!