A quote:
Este es un test
a lovely quiet paragraph no code exists still a valid knitr document
-- My haiku on an empty document
La media de los pesos de 500 estudiantes de un colegio es 70 kg y la desviacion tipica 3 kg. Suponiendo que los pesos se distribuyen normalmente, hallar cuantos estudiantes pesan: es nuestra valor <!--
options(digits = 7) fit <- lm(regFormula(), data = mtcars) b <- coef(fit) summary(fit) #input$area_CalDis #library(shiny) #textOutput("area_CalDis")
-->
The fitting result is $mpg = r b[1]
+ r b[2]``r input$x
$.
Below is a scatter plot with the regression line.
par(mar = c(4, 4, 1, 1)) plot(regFormula(), data = mtcars, pch = 19, col = 'gray') abline(fit, col = 'red', lwd = 2)
We examine the relationship between speed and stopping distance using a linear regression model: $Y = \beta_0 + \beta_1 x + \epsilon$.
par(mar = c(4, 4, 1, 1), mgp = c(2, 1, 0), cex = 0.8) plot(cars, pch = 20, col = 'darkgray') fit <- lm(dist ~ speed, data = cars) abline(fit, lwd = 2)
The slope of a simple linear regression is r coef(fit)[2]
.
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