Data from the Danish Welfare Study.

data("DanishWelfare")

Format

A data frame with 180 observations and 5 variables.

Freq

frequency.

Alcohol

factor indicating daily alcohol consumption: less than 1 unit (<1), 1-2 units (1-2) or more than 2 units (>2). 1 unit is approximately 1 bottle of beer or 4cl 40% alcohol.

Income

factor indicating income group in 1000 DKK (0-50, 50-100, 100-150, >150).

Status

factor indicating marriage status (Widow, Married, Unmarried).

Urban

factor indicating urbanization: Copenhagen (Copenhagen), Suburbian Copenhagen (SubCopenhagen), three largest cities (LargeCity), other cities (City), countryside (Country).

References

E. B. Andersen (1991), The Statistical Analysis of Categorical Data. 2nd edition. Springer-Verlag, Berlin.

Source

E. B. Andersen (1991), The Statistical Analysis of Categorical Data, page 205.

Examples

data("DanishWelfare")
ftable(xtabs(Freq ~ ., data = DanishWelfare))
#>                           Urban Copenhagen SubCopenhagen LargeCity City Country
#> Alcohol Income  Status                                                         
#> <1      0-50    Widow                    1             4         1    8       6
#>                 Married                 14             8        41  100     175
#>                 Unmarried                6             1         2    6       9
#>         50-100  Widow                    8             2         7   14       5
#>                 Married                 42            51        62  234     255
#>                 Unmarried                7             5         9   20      27
#>         100-150 Widow                    2             3         1    5       2
#>                 Married                 21            30        23   87      77
#>                 Unmarried                3             2         1   12       4
#>         >150    Widow                   42            29        17   95      46
#>                 Married                 24            30        50  167     232
#>                 Unmarried               33            24        15   64      68
#> 1-2     0-50    Widow                    3             0         1    4       2
#>                 Married                 15             7        15   25      48
#>                 Unmarried                2             3         9    9       7
#>         50-100  Widow                    1             1         3    8       4
#>                 Married                 39            59        68  172     143
#>                 Unmarried               12             3        11   20      23
#>         100-150 Widow                    5             4         1    9       4
#>                 Married                 32            68        43  128      86
#>                 Unmarried                6            10         5   21      15
#>         >150    Widow                   26            34        14   48      24
#>                 Married                 43            76        70  198     136
#>                 Unmarried               36            23        48   89      64
#> >2      0-50    Widow                    2             0         2    1       0
#>                 Married                  1             2         2    7       7
#>                 Unmarried                3             0         1    5       1
#>         50-100  Widow                    3             0         2    1       3
#>                 Married                 14            21        14   38      35
#>                 Unmarried                2             0         3   12      13
#>         100-150 Widow                    2             1         1    1       0
#>                 Married                 20            31        10   36      21
#>                 Unmarried                0             2         3    9       7
#>         >150    Widow                   21            13         5   20       8
#>                 Married                 23            47        21   53      36
#>                 Unmarried               38            20        13   39      26