patrones<-read.csv("EduardoCal.csv")
knitr::kable(patrones, align = "lc")
| 2.961004 |
3718453 |
| 6.636550 |
5698866 |
| 7.128343 |
6165431 |
| 15.959615 |
9791255 |
| 17.216873 |
10578222 |
| 38.759065 |
16215582 |
| 41.583397 |
18278200 |
| 100.000000 |
39114734 |
| 100.000000 |
38054828 |
Curva de Calibración del ciproconazol
y<-patrones$Area
x<-patrones$Conc
modelo<-lm(y~x)
s<-summary(modelo)
LOD<-s$coefficients[1,2]/s$coefficients[1]*3.3*8
b0=s$coefficients[1]
b1=s$coefficients[2]
yexp=modelo$fitted.values
Sxy=sqrt((sum(y*y)-b0*sum(y)-b1*sum(x*y))/(length(x)-2))
print(s)
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -909181 -510642 114716 549264 995471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3555052 342259 10.39 1.66e-05 ***
## x 350104 6644 52.70 2.32e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 720700 on 7 degrees of freedom
## Multiple R-squared: 0.9975, Adjusted R-squared: 0.9971
## F-statistic: 2777 on 1 and 7 DF, p-value: 2.322e-10
n=length(x)
SE=sqrt(sum((y-yexp)**2)/(n-2))
print("LOD:")
## [1] "LOD:"
print(SE/modelo$coefficients[2]*3.3)
## x
## 6.793209
t<-qt(0.975,3)
x1<-seq(0,100,0.1)
y1<-modelo$coefficients[2]*x1+modelo$coefficients[1]
IC1<-y1+t*Sxy*sqrt(1/n+(x1-mean(x))**2/sum((x1-mean(x))**2))
IC2<-y1-t*Sxy*sqrt(1/n+(x1-mean(x))**2/sum((x1-mean(x))**2))
plot(x,y,main="Curva de calibración del metano",xlab="[%Metano](v/v)",ylab="Area")
lines(x1,y1,lty=1,col="black")
lines(x1,IC1,lty=2,col="blue")
lines(x1,IC2,lty=2,col="blue")
library(ggplot2)
ggplot(patrones,aes(Conc, Area)) +
geom_point() +
geom_smooth(method='lm')
## `geom_smooth()` using formula 'y ~ x'
