Hi, and welcome!
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Let's use mtcars.
fit <- lm(mpg ~ wt, data = mtcars)
summary(fit)
#>
#> Call:
#> lm(formula = mpg ~ wt, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -4.5432 -2.3647 -0.1252 1.4096 6.8727
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 37.2851 1.8776 19.858 < 2e-16 ***
#> wt -5.3445 0.5591 -9.559 1.29e-10 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.046 on 30 degrees of freedom
#> Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446
#> F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10
par(mfrow=c(2,2))
plot(fit)
Created on 2020-03-06 by the reprex package (v0.3.0)