Create a function in R to perform a two-tailed one sample t-test. Assume that po

Question

Create a function in R to perform a two-tailed one sample t-test. Assume that population SD is unknown. Find the p-value for H0: mu=100 vs H1: mu does not = 100.
*** I am not supposed to use the ttest function. I'm supposed to create my own function to solve this problem.
The format may be similar to something like this:
fn.name = function(-,-,...) { test stat = [mean()-.......] p-value = ... print(p-value) } Create a function in R to perform a two-tailed one sample t-test. Assume that population SD is unknown. Find the p-value for H0: mu=100 vs H1: mu does not = 100.
*** I am not supposed to use the ttest function. I'm supposed to create my own function to solve this problem.
The format may be similar to something like this:
fn.name = function(-,-,...) { test stat = [mean()-.......] p-value = ... print(p-value) }
*** I am not supposed to use the ttest function. I'm supposed to create my own function to solve this problem.
The format may be similar to something like this:
fn.name = function(-,-,...) { test stat = [mean()-.......] p-value = ... print(p-value) }

Explanation / Answer

R code:

#x is a vector containing the data
#m0 is the hypothesized population mean
t.test1Sample <- function(x,m0){
        m1 = mean(x)
        s = sd(x)
        n = length(x)
        se = s / sqrt(n)
        df = n - 1
        t = (m1-m0)/se
        p = 2*pt(-abs(t),df)
        data <- c(m1-m0, se, t, p)
        names(data) <- c("Difference of means", "Std Error", "t", "p-value")
        return(data)
}

Trial output:

Please note: the entire sample as a vector is the input for this function. If required, this can be modified to provide the sample mean, sample standard deviation and the length of the sample.

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