manpower {bestglm} | R Documentation |

## Hospital manpower data

### Description

The goal of this study is to predict the manpower requirement as given in
the output
variable Hours given the five other input variables.
Data is from Table 3.8 of Myers (1990). See also Examples 3.8, 4.5, 8.8.

### Usage

`data(manpower)`

### Format

A data frame with 17 observations. The output variable is Hours and the
inputs are Load, Xray, BedDays, AreaPop and Stay. The site 1 through 17
is indicated by the row name.

`Load`

a numeric vector

`Xray`

a numeric vector

`BedDays`

a numeric vector

`AreaPop`

a numeric vector

`Stay`

a numeric vector

`Hours`

a numeric vector

### Details

This data illustrates the multicollinearity problem and the use of VIF to
identify it. It provides an illustrative example for ridge regression and
more modern methods such as lasso and lars.

### Source

Myers (1990) indicates the source was
"Procedures and Analysis for Staffing Standards Development:
Data/Regression Analysis Handbook",
Navy Manpower and Material Analysis
Center, San Diego, 1979.

### References

Myers, R. (1990). Classical and Modern Regression with Applications.
The Duxbury Advanced Series in Statistics and Decision Sciences.
Boston: PWS-KENT Publishing Company.

### Examples

```
data(manpower)
```

[Package

*bestglm* version 0.37.3

Index]