RxC table concerns a basic two dimensional crosstable procedure. The procedure matches the values of two variables and counts the number of occasions that pairs of values occur. It then presents the result in tables and allows for various statistical tests.

One case per row individual level data has to be given in two columns, one column for the table rows and one for the table columns. Separators between the two columns can be spaces, returns, semicolons, colons and tabs. Any format within the columns will do. Both numbers, letters and words will be read and classified. Numbers are treated by name, thus 10 and 10.0 are in different categories and 5 is larger than 12. For table input you have to give the number of rows and columns in your table and the table is read unstructured, row after row. The input is presumed to consist of whole counted integer numbers without decimals or scientific notation. Seperators between numbers can be spaces, commas, dots, semicolons, colons, tabs, returns and linefeeds.

To guess the name of a man on the basis of the weighted table below, and the only information we have is the distribution of mans' names in the sample, we would guess John, with a (38+34)/113*100=63.7% chance of an erroneous guess. However, if we know the name of mans' partners, we would guess John if the partner is Liz, with a (10+8)/41*100=43.9% chance of an error, Peter if the partner is Mary (44.7% errors), Steve if the partner is Linda (58.8%). The average reduction in errors in the row marginal, weighted by cell size (Lambda-B), equals 23.6%, the average weighted error rate in guessing a man's name after knowing the women's name, equals 63.7*(1-0.236)=48.7%. This 48.7% can also be calculated as: (10+8+6+11+8+12)/113. With a p-value of 0.00668 we significantly improve our probability of guessing a man's name correctly, after considering the name of the man's partner. Same for guessing a woman's name, only now you have to use the Lambda–A. Lambda is always positive, and the significance test always single sided, because information on the inside of the table will always lead to an improvement compared with knowing only the marginal.

If you copy and paste the following data into the input field:

john marypeter linda

john liz

steve mary

john linda

steve mary

steve linda

You get the following table:

Table of Counts | ||||

linda | liz | mary | Sum | |

john | 1 | 1 | 1 | 3 |

peter | 1 | 0 | 0 | 1 |

steve | 1 | 0 | 2 | 3 |

Sum | 3 | 1 | 3 | 7 |

Pearson: 3.111 (p= 0.53941). There is no statistically significant relationship between between boys names and girls names, although this conclusion has to be viewed with care as the table is based on very few observations.

You could count (in a flat table) how often each of the pairs of names occurs in a sample, and weigh each of the pairs with these counts.

john linda 10john liz 23

john mary 8

peter linda 6

peter liz 11

peter mary 21

steve linda 14

steve liz 8

steve mary 12

And you get the following table:

Table of Weights | ||||

linda | liz | mary | Sum | |

john | 10 | 23 | 8 | 41 |

peter | 6 | 11 | 21 | 38 |

steve | 14 | 8 | 12 | 34 |

Sum | 30 | 42 | 41 | 113 |

Weighted Pearson: 17.77 (p= 0.00137). After considering how often pairs of names occur in a sample there is a highly significant relation between certain boys and certain girls names.

The formatting and tabulating of large data sets might take a while in which case there might be warnings, just select "continue" and in the end the computer will get there.

The procedure is meant for relatively small tables. Number of cells is in principle limited to 120, but might be less dependent on your browser and other settings. Is also rather less with weighted data as more info has to be transferred.