One Sample, one sided T-test

[FelixIrmentrud]

Hello people,

I am new in R and for my class, I am trying to write the codes for one sample, a one-sided t-test for the alternative B<0. As you probably can understand, weight is the independent variable I want to test.
I wrote:

t.test(Data$weight, alternative = "less", mu = 0, paired = FALSE, var.equal = FALSE)

For some mistake I did, I am receiving a p value=1 where it is certainly should be 0.1.

I know I can find the p-value from the two-sided test but I want to be able to do it this way.
Would you please point out my mistake or suggest a fix, a better option?

[jrkrideau]

@FelixIrmentrud

t.test(Data$weight, alternative = "less", mu = 0, paired = FALSE, var.equal = FALSE)

I suspect it may be that you cannot have a negative weight.

[FelixIrmentrud]

I would like to test the coefficients to check the significance of the independent variable. But I think this is the wrong test for that.

[jrkrideau]

I think you need to give us some background on what you are doing and some sample data.

See a reproducible example (reprex)

FAQ: What's a reproducible example (`reprex`) and how do I create one?

Why reprex? Getting unstuck is hard. Your first step here is usually to create a reprex, or reproducible example. The goal of a reprex is to package your code, and information about your problem so that others can run it and feel your pain. Then, hopefully, folks can more easily provide a solution. What's in a Reproducible Example? Parts of a reproducible example: background information - Describe what you are trying to do. What have you already done? complete set up - include any library() calls and data to reproduce your issue. data for a reprex: Here's a discussion on setting up data for a reprex make it run - include the minimal code required to reproduce your error on the data…

A handy way to supply sample data is to use the dput() function. See ?dput. If you have a very large data set then something like head(dput(myfile), 100) will likely supply enough data for us to work with.

[rwalker]

t.test(Data$weight, alternative = "less", mu=0)

should work. Not sure why it is not, though the paired or not paired and var.equal flags are irrelevant to a one-sample t.test.

[FelixIrmentrud]

I just put them down just in case but I guess this specific test is about testing the mean of the independent variable rather than its coefficient and significance. That is what I have been told. Thank you.

[rwalker]

If this answers the specific question that you posed, for the good of the community, you could choose an answer and close the thread.