# hw: Height-Weight Covariance Study In ACSWR: A Companion Package for the Book "A Course in Statistics with R"

## Description

The data set highlights the importance of handling covariance when such information is available. If the covariance is not incorporated, hypothesis testing may lead to entirely difference conclusion.

## Usage

 `1` ```data(hw) ```

## Format

A data frame with 20 observations on the following 2 variables.

`Height`

the height of an individual

`Weight`

the weight of an individual

## References

Rencher, A.C. (2002). Methods of Multivariate Analysis, 2e. J. Wiley.

## Examples

 ```1 2 3 4 5 6 7 8``` ```data(hw) sigma0 <- matrix(c(20, 100, 100, 1000),nrow=2) sigma <- var(hw) v <- nrow(hw)-1 p <- ncol(hw) u <- v*(log(det(sigma0))-log(det(sigma)) + sum(diag(sigma%*%solve(sigma0)))-p) u1 <- (1- (1/(6*v-1))*(2*p+1 - 2/(p+1)))*u u;u1;qchisq(1-0.05,p*(p+1)/2) ```

### Example output

```[1] 11.09374
[1] 10.66832
[1] 7.814728
```

ACSWR documentation built on May 2, 2019, 6:53 a.m.