Linear regression makes it easier to understand how different factors relate to a certain outcome. It looks at how a dependent variable (the one we want to predict) is influenced by one or more independent variables (the factors we think affect it) using a straight-line equation.

Key Points:

• How the Model Works:

  The equation for a linear regression model looks like this:

  $Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + ... + \beta_nX_n + \epsilon$

  Here, $Y$ stands for the dependent variable we want to predict.

  The $X_i$ values are the independent variables (the ones we think will affect $Y$).

  The $\beta_i$ values are the coefficients, which show how much $Y$ changes when one of the $X$ values changes.

  The $\epsilon$ part shows that there is some error or randomness we can’t fully explain.

• Easy to Understand:

  The coefficients ($\beta_i$) tell us how much the dependent variable ($Y$) will change when one of the independent variables changes by one unit. This makes it pretty simple to understand the results.

• How We Measure Success:

  • R-squared ($R^2$): This tells us how well our model explains what happens with the dependent variable. It is a number between 0 and 1. A higher number means the model does a good job.

  • Root Mean Square Error (RMSE): This number gives us an average of how big the prediction errors are. It helps us see how accurate our model is.

In summary, linear regression helps us understand and predict outcomes by looking at how different factors relate to each other in a simple way.