Rank Sum After ranking the combined list of female heights given in Exercise 1, find the sum of the ranks for the ANSUR I sample.

Verified step by step guidance

1. Step 1: Combine the heights from both groups (ANSUR I sample and the other sample) into a single list. Ensure all values are included and properly recorded.
2. Step 2: Rank the combined list of heights in ascending order. Assign ranks starting from 1 for the smallest value, and continue sequentially. If there are ties (identical values), assign the average rank to each tied value.
3. Step 3: Identify the ranks corresponding to the heights from the ANSUR I sample within the combined ranked list.
4. Step 4: Sum the ranks of the ANSUR I sample by adding together all the ranks identified in the previous step.
5. Step 5: Verify the sum by double-checking the ranks and ensuring no values were missed or incorrectly added.

Key Concepts

Rank Sum

Rank sum refers to the total of the ranks assigned to a set of observations in a dataset. In statistical analyses, particularly non-parametric tests like the Mann-Whitney U test, the rank sum is used to compare groups by evaluating the order of data rather than their actual values. This method is useful when the data does not meet the assumptions of normality required for parametric tests.

Ranking

Ranking is the process of ordering data points based on their values, typically from lowest to highest. In the context of heights, each individual is assigned a rank based on their height relative to others in the dataset. Ties in values are usually handled by assigning the average rank to the tied values, ensuring that the ranking reflects the relative positions of all observations.

ANSUR I Sample

The ANSUR I sample refers to a specific dataset collected as part of the Anthropometric Survey of U.S. Army Personnel, which includes measurements such as height. This dataset is often used in statistical analyses to understand physical characteristics of a population. When analyzing the rank sum for this sample, it is important to consider the context of the data and its implications for the population being studied.