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)
```

*bestglm*version 0.37.3 Index]