Multiple Regression with R [closed]

You can run a regression on R using the lm function. Using the mtcars base that already comes in R as an example:

regressao <- lm(mpg ~ cyl, data = mtcars)

First we move on to the lm function of the mpg ~ cyl regression, and then the data = mtcars database. The formula mpg ~ cyl means that we are regressing the variable mpg (miles per gallon) against the variable cyl (displacements), it would be equivalent to the equation mph = b0 + b1 * cyl + e, and you are estimating the parameters b0 (constant) and b1 (angular coefficient). The regression result was saved in object regressao .

When giving summary you see the main results of the regression:

summary(regressao)

Call:
lm(formula = mpg ~ cyl, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9814 -2.1185  0.2217  1.0717  7.5186 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.8846     2.0738   18.27  < 2e-16 ***
cyl          -2.8758     0.3224   -8.92 6.11e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.206 on 30 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7171 
F-statistic: 79.56 on 1 and 30 DF,  p-value: 6.113e-10

To perform a multiple regression, simply add more variables after ~ . More specifically, the element to the left of ~ is the dependent variable (its y) and all variables to the right of ~ are explanatory variables (the Xs). For example:

regressao_multipla <- lm(mpg ~ cyl + disp + wt + hp , data = mtcars)

Here we run a regression with 4 explanatory variables: cyl , disp , wt and hp , all data.frame mtcars. To see the top results, use summary again:

summary(regressao_multipla)
Call:
lm(formula = mpg ~ cyl + disp + wt + hp, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0562 -1.4636 -0.4281  1.2854  5.8269 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 40.82854    2.75747  14.807 1.76e-14 ***
cyl         -1.29332    0.65588  -1.972 0.058947 .  
disp         0.01160    0.01173   0.989 0.331386    
wt          -3.85390    1.01547  -3.795 0.000759 ***
hp          -0.02054    0.01215  -1.691 0.102379    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.513 on 27 degrees of freedom
Multiple R-squared:  0.8486,    Adjusted R-squared:  0.8262 
F-statistic: 37.84 on 4 and 27 DF,  p-value: 1.061e-10

There are several other functions to work with regressions in R. The object that the lm function returns is of the lm class, so you have an idea of the methods available for the class you can run methods(class = "lm") .