What happens when we introduce more variables to a linear regression model? Let’s consider the following regression model:
$y = B_{0} + B_{1}*x$
where

*

*$B_{0}$ — represents the intercept

*$B_{1}$ — represents the coefficient

*$x$ — represents the independent variable

*$y$ — represents the output or the dependent variable

or
Multiple linear regression:

Mathematically, R-squared is calculated by dividing the sum of squares of residuals ($SS_{res}$) by the total sum of squares ($SS_{tot}$) and then subtract it from 1. In this case, $SS_{tot}$ measures the total variation. $SS_{res}$ measures explained variation and $SS_{res}$ measures the unexplained variation.
As $SS_{res}+ SS_{res} = SS_{tot}$
$R² = Explained variation / Total Variation$

Adjusted R-Squared can be calculated mathematically in terms of the sum of squares. The only difference between R-square and the Adjusted R-square equation is the degree of freedom.

In the above equation, $df_{t}$ is the degrees of freedom $n– 1$ of the estimate of the population variance of the dependent variable, and $df_{e}$ is the degrees of freedom $n – p – 1$ of the estimate of the underlying population error variance.
Adjusted R-squared value can be calculated based on the value of r-squared, the number of independent variables (predictors), total sample size.


What happens when we introduce more variables to a linear regression model in terms of $R^2$ and adjusted $R^2$?
Will they increase, decrease, or remain constant?

 A: If you introduce more variables, the $R^2$ will always increase, it can never decrease. This follows mathematically from the observation that
$$ (y-\beta_0-\beta_1 x_1-...-\beta_p x_p-\beta_{p+1} x_{p+1})^2 \leq(y-\beta_0-\beta_1 x_1-...-\beta_p x_p)^2$$
On the other hand, the adjusted $R^2$ makes an adjustement for the number of variables. It will typically increase if your new variable is highly correlated to your response $Y$ and decrease if this new variable is only slightly correlated to your response. Therefore, it is considered as a better measure that standard $R^2$ because the $R^2$ will tend to always increase with the number of new variables. 
A: A very important effect of adding more variables is that you can better adjust for confounding, which means better causal interpretation. Which sometimes might be more important than looking at the $R^2$.
For example, imagine we want a regression of the form $Y = X\beta + \epsilon$ where $X$ and $Y$ is thought of as a random variable. Suppose  now that there is some unobserved random variable $Z$ that is related to both $X$ and $Y$ such as in the picture below taken from Wikipedia:

Then even if $X$ has no direct effect on $Y$, if you don't include $Z$ in the regression you will most likely observe a significant value for $\beta$. After including $Z$, the coefficient of $X$ will be closer to only reflect the effect that is not explained by $Z$.
A: Adding more variables will increase R Squared whether or not the added variables have any statistically significant effect on the dependent variable. On the other hand, adjusted R Squared can increase or decrease.
