R notes for Activity 23-1: Marriage Ages

You can read the data with

```MA = read.csv("/home/rebelsky/Stats115/Data/MarriageAges.csv")
```

23-1 a. A Two-Sample t-test

This is a one-sided t-test, which we can express with

```t.test(MA\$HusbandAge,MA\$WifeAge,alternative="greater")
```

23-1 b. Computing a Confidence Interval

You should be able to figure out how tocompute this confidence interval based on The R Notes for Topic 22.

23-1 c. Scatterplots

You can produce a similar scatterplot using

```plot(MA\$WifeAge,MA\$HusbandAge)
```

You can add the “same age” line with

```abline(0,1)
```

23-1 e. A Confidence Intervals for Differences

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)
```

23-1 g. A Paired t-test

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")
```

23-1 i. Exploring Technical Conditions

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)
```

Samuel A. Rebelsky, rebelsky@grinnell.edu

Copyright (c) 2007-8 Samuel A. Rebelsky.

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