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# 27.1: Regresión lineal (Sección 26.1)

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Para realizar regresión lineal en R, utilizamos la función lm (). Generemos algunos datos y utilicemos esta función para calcular la solución de regresión lineal.

npoints <- 100
intercept = 10
# slope of X/Y relationship
slope=0.5
# this lets us control the strength of the relationship
# by varying the amount of noise added to the y variable
noise_sd = 0.6

regression_data <- tibble(x = rnorm(npoints)) %>%
mutate(y = x*slope + rnorm(npoints)*noise_sd + intercept)

ggplot(regression_data,aes(x,y)) +
geom_point()

Luego podemos aplicar lm () a estos datos:

lm_result <- lm(y ~ x, data=regression_data)
summary(lm_result)
##
## Call:
## lm(formula = y ~ x, data = regression_data)
##
## Residuals:
##     Min      1Q  Median      3Q     Max
## -1.5563 -0.3042 -0.0059  0.3804  1.2522
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   9.9761     0.0580  172.12  < 2e-16 ***
## x             0.3725     0.0586    6.35  6.6e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.58 on 98 degrees of freedom
## Multiple R-squared:  0.292,  Adjusted R-squared:  0.284
## F-statistic: 40.4 on 1 and 98 DF,  p-value: 6.65e-09

Deberíamos ver tres cosas en los resultados de lm ():

• La estimación de la Intercepción en el modelo debe estar muy cerca de la intercepción que especificamos
• La estimación para el parámetro x debe estar muy cerca de la pendiente que especificamos
• El error estándar residual debe ser aproximadamente similar a la desviación estándar de ruido que especificamos

This page titled 27.1: Regresión lineal (Sección 26.1) is shared under a not declared license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.