Equation of Linear Regression
Depending on what language you are using you can just print the model. In R it looks like this:

summary(my.model)


The output will look like this, or similar:

## 
## Call:
## lm(formula = dist ~ speed.c, data = cars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.069  -9.525  -2.272   9.215  43.201 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  42.9800     2.1750  19.761  &lt; 2e-16 ***
## YearsExp     3.9324     0.4155   9.464 1.49e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared:  0.6511, Adjusted R-squared:  0.6438 
## F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12


Your betas are under the "Estimate Std." Column. Your beta0 is (Intercept) and your beta1 is YearsExp (or whatever your variable is) etc... If you have more than one variable there will be more in this column for you to see.

After you get the betas you can write a function to apply your model to new data like this:

y-hat &lt;- 42.9800 + YearsExp*3.9.324


Model summary copypastad w/ variable name edit from: https://feliperego.github.io/blog/2015/10/23/Interpreting-Model-Output-In-R