RClase02

datos=c(6,3,7,4,5,3)
A=matrix(data=datos, nrow=3, ncol=2, byrow=FALSE)
print(A*3)

##      [,1] [,2]
## [1,]   18   12
## [2,]    9   15
## [3,]   21    9

matA<-matrix(data=c("a","b","c","d","e","f"), nrow=3, ncol=2, byrow=TRUE)
rownames(matA)<-c("Fila1", "Fila2", "Fila3")
colnames(matA)<-c("Col1", "Col2")
print(matA)

##       Col1 Col2
## Fila1 "a"  "b" 
## Fila2 "c"  "d" 
## Fila3 "e"  "f"

Fórmulas estadiasticas

x<-c(4,2,7,8,4,6,5,8,3)
media<-mean(x)
desvSTD<-sd(x)
varianza<-var(x)

print(media)

## [1] 5.222222

print(desvSTD)

## [1] 2.166667

print(varianza)

## [1] 4.694444

x<-c(0,1,2,3,4,5)
y<-c(0.5,1.4,1.98,3.1,3.8,5.4)
formula1<-formula(y~x)
modelo<-lm(formula1)
summary(modelo)

## 
## Call:
## lm(formula = formula1)
## 
## Residuals:
##        1        2        3        4        5        6 
##  0.14762  0.10990 -0.24781 -0.06552 -0.30324  0.35905 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.35238    0.20485    1.72 0.160511    
## x            0.93771    0.06766   13.86 0.000157 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.283 on 4 degrees of freedom
## Multiple R-squared:  0.9796, Adjusted R-squared:  0.9745 
## F-statistic: 192.1 on 1 and 4 DF,  p-value: 0.0001571

plot(x,y)
abline(0.35238, 0.93771, col="blue")

altura<-c(2.25,2.00,1.80,1.70,1.60,1.50,1.25) 
tiempo<-c(0.729,0.631,0.623,0.607,0.586,0.553,0.503)
h<-seq(1.0,2.50,0.1)
tiempoCalc<-sqrt(h*2/9.8)
plot(tiempo,altura, xlim=c(0.5,0.75),ylim=c(1.0,2.5), main="Altura(cm) vs tiempo (s)", xlab="t(seg)", ylab="altura(cm)") 
lines(tiempoCalc,h,col="black")
abline(-0.8010,4.1449,col="purple")
abline(v=0.7, col="green")
abline(h=2.0, col="pink")
abline(h=0.5, col="yellow")
abline(v=0.5, col="red")

modelo<-lm(altura~tiempo)
summary(modelo)

## 
## Call:
## lm(formula = altura ~ tiempo)
## 
## Residuals:
##        1        2        3        4        5        6        7 
## -0.04588  0.15093 -0.01259 -0.03964 -0.04390  0.00656 -0.01547 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.0279     0.2672  -3.846 0.012048 *  
## tiempo        4.5593     0.4395  10.374 0.000143 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07593 on 5 degrees of freedom
## Multiple R-squared:  0.9556, Adjusted R-squared:  0.9467 
## F-statistic: 107.6 on 1 and 5 DF,  p-value: 0.0001433

idealizando \(x2\)

altura<-c(2.25,2.00,1.80,1.70,1.60,1.50,1.25) 
tiempo<-c(0.729,0.631,0.623,0.607,0.586,0.553,0.503)
h<-seq(1.0,2.50,0.1)
tiempoCalc<-sqrt(h*2/9.8)

t2=tiempo*tiempo

plot(t2,altura,main="Altura(cm) vs t2(s2)")
abline(0.4698,3.3319)

modelo<-lm(altura~t2)
summary(modelo)

## 
## Call:
## lm(formula = altura ~ t2)
## 
## Residuals:
##         1         2         3         4         5         6         7 
## -0.070195  0.167535  0.004247 -0.023735 -0.032055  0.005492 -0.051289 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.3754     0.1528   2.456 0.057500 .  
## t2            3.6594     0.4038   9.062 0.000274 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08633 on 5 degrees of freedom
## Multiple R-squared:  0.9426, Adjusted R-squared:  0.9311 
## F-statistic: 82.12 on 1 and 5 DF,  p-value: 0.0002736

\(/sigma\)

\(/alpha\)

\(/alpha^{3x}\)

\(/frac{3a}{1-x}\)

\(/sqrt{9x^2}\)

\(/int{/frac{3a}{1-x}}\)