# HM 1

Due: Tues, 1/28.

1. Successfully install R.

2. When I work on a Windows box, I like to put my data in an "R\\data"
into R with the scan command. For my setup,
dat=scan("..\\data\\hm1.dat") is the command. Working directory set by: setwd("C:/Users/rouderj/Desktop/ProcMod")

3. dat is a vector. find out how many scores are in it (hint, use
this command. You can type help.start() for an html help system.

4. Find the sample mean, sample variance, sample standard deviation,
sample median, and sample deciles. Hint: use
mean,var,sd,median,quantile. Dont forget, you can type help(mean)
commands.

5. Draw a histogram of the data. Hint: hist. Is the data like a normal?

6. Is the mean of the sample significantly different from 90.0? Hint:
use t.test.

OK lets simulate some data:

7. Make x be a vector of 200 samples from a normal with mean of 100
and standard deviation of 15 make y be a vector of 200 samples from a
normal witth mean of 105 and standard deviation of 15. Hint: rnorm

Now, pretend you know nothing about the parameters used to make the data.

8. What is the difference between the sample means?

9. How substantial is it relative to the variability in the data?
Hint: divide difference between sample means by some pooled
estimate of standard deviation.

10. How statistically significant is the difference in sample means.
Use t.test with var.equal=T option. Lets see if we can plot
both samples on the same graph. It will be too messy to do with
histograms. Instead, I like box plots. They arent as detailed as
histograms but they quickly display distributional information.
Try boxplot(x,y). What do the various components denote
(midline, box, wiskers, points)?

AttachmentSize
hm1.dat300 bytes