Hi Everyone. Just wanted to know that How you guys usually charge your students? Is it by "Per Hour" or "per Month" or for "Entire Course"? And if its is by "per Month" or for "Entire Course" basis, then How many classes do you take in a week?

Shapiro-Wilk normality test

data: y
W = 0.93333, p-value = 7.726e-05

>
> par(mfrow=c(1, 1))
> boxplot(x, y)
> boxplot(x, y,col = 101)
> boxplot(x, y,col = 101,size = n)
Error in boxplot.default(x, y, col = 101, size = n) :
object 'n' not found
> (library(foreign))
[1] "foreign" "stats" "graphics" "grDevices"
[5] "utils" "datasets" "methods" "base"
> (library(shapefiles))
Error in library(shapefiles) : there is no package called ‘shapefiles’
> attach(mydata)
Error in attach(mydata) : object 'mydata' not found
> group1 <- leng[klett == 0.25 & temp == 11]
Error: object 'leng' not found
> group2 <- leng[klett == 1 & temp == 11]
Error: object 'leng' not found
> group3 <- leng[klett == 16 & temp == 11]
Error: object 'leng' not found
> # ....
> boxplot(group1, group2, group3, names=c("0.25/11", "1/11", "16/11"))
Error in boxplot(group1, group2, group3, names = c("0.25/11", "1/11", :
object 'group1' not found
> detach(mydata)
Error in detach(mydata) : invalid 'name' argument
> plot(1:30, qt(0.975, 1:30), type="l",
+ ylab="Student´s t", xlab="d.f.", ylim=c(0,15))
> abline(h=qnorm(0.975), lty="dotted")
>
> hist(log(x), probability=TRUE)
Warning message:
In log(x) : NaNs produced
> xnew <- seq(log(min(x)), log(max(x)), length=100)
Error in seq.default(log(min(x)), log(max(x)), length = 100) :
'from' must be a finite number
In addition: Warning message:
In log(min(x)) : NaNs produced
> lines(xnew, dnorm(xnew, mean=mean(log(x)), sd=sd(log(x))), col="red")
Error in lines(xnew, dnorm(xnew, mean = mean(log(x)), sd = sd(log(x))), :
object 'xnew' not found
> x <- seq(0.01, 4, length=100)
> par(mfrow=c(2, 2))
> plot(x, dgamma(x, .5, .5), type="l")
> plot(x, dgamma(x, .8, .8), type="l")
> plot(x, dgamma(x, 2, 2), type="l")
> plot(x, dgamma(x, 10, 10), type="l")
>
> x1 <- rnorm(100, mean = 50, sd = 10) # normal distribution
> x2 <- runif(100, min = 30, max = 70) # uniform distribution
> x3 <- rlnorm(100, meanlog = 2, sdlog = 1) # lognormal distribution
> boxplot(x1, x2, x3,
+ names=c("Normal", "Uniform", "Lognormal"))
>
> x1 <- c(2, 3, 4, 5, 6)
> x2 <- c(3, 4, 7, 6, 8)
> t.test(x1, x2, var.equal=TRUE) # p=0.20

Two Sample t-test

data: x1 and x2
t = -1.372, df = 8, p-value = 0.2073
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.28924 1.08924
sample estimates:
mean of x mean of y
4.0 5.6