Introduction to Statistics (MAT/SST 115.03 2008S)
You can read the data with
MA = read.csv("/home/rebelsky/Stats115/Data/MarriageAges.csv")
This is a one-sided t-test, which we can express with
You should be able to figure out how tocompute this confidence interval based on The R Notes for Topic 22.
You can produce a similar scatterplot using
You can add the “same age” line with
We can use a one-sample t-test of the difference between the two columns.
t.test(MA$HusbandAge-MA$WifeAge, mu=0, conf.level=.9)
Once again, we need to tell R that this is a one-sided t-test.
t.test(MA$HusbandAge-MA$WifeAge, mu=0, alternative="greater")
We might want to make a normal probability plot to see if the data are normal. I'm not sure why, but R gives a somewhat different plot than does the answer key.
> qqnorm(MA$HusbandAge-MA$WifeAge, datax=T) > qqline(MA$HusbandAge-MA$WifeAge, datax=T)
If all goes well, there will be three files of melting time:
Chips.csv. You can load them with
CChips = read.csv("/home/rebelsky/Stats115/Data/CChips.csv") BChips = read.csv("/home/rebelsky/Stats115/Data/BChips.csv") Chips = read.csv("/home/rebelsky/Stats115/Data/Chips.csv")
You should be able to use your notes on earlier activities to compute the appropriate summaries. Ask for help if you need it.
Although the book does not provide the data in electronic format,
I've put it into the file
MuscleFatigue.csv for you. You can load
MF = read.csv("/home/rebelsky/Stats115/Data/MuscleFatigue.csv")
To compute the difference between men and women, you would use
To build a dotplot for these data, you would use
library(BHH2, lib="/home/rebelsky/Stats115/Packages") dotPlot(MF$Man-MF$Woman)
You should be able to figure out how to do the t-test and confidence interval.
Copyright (c) 2007-8 Samuel A. Rebelsky.
This work is licensed under a Creative Commons
Attribution-NonCommercial 2.5 License. To view a copy of this
or send a letter to Creative Commons, 543 Howard Street, 5th Floor,
San Francisco, California, 94105, USA.