Research Paper on:
Principles of Univariate Data Analysis

Unformatted Sample Text from the Research Paper:
that the collection and dissemination of this gathered information can sometimes be a frustrating matter. Statistics covers many issues from abstract areas such as hypothesis testing and parameter estimation to  more down to earth problems such as univariate data analysis. In a social research setting, which involves segments of the population, gathering good data may not be necessarily easy to  come by. A true random sampling could be time consuming and taking the reliable measurements may factor in, unwittingly, sampler bias. The univariate data analysis, simply put, makes predictions on  the distribution of a single variable. This takes into account the measures of central tendency, including mean, median and mode, as well as measures of dispersion, including range, variance and  standard deviation. A prime example of how univariate analysis examines only one variable at a time is to hand out a photocopy of someones hand. Distribute  this to one hundred people. Assign each person the same task of measuring the index finger and the thumb, then reporting back with their answers. The answers will predictably be  different. Why? The length of the thumb will be the major variable since there is no obvious place where the thumb stops and the hand begins. Thus, reliability and validity  of measurement are called into question. Parameters must be set. However, the researcher could choose to report more than just the marginals in this example. Central tendency or summary  averages include the mode, which is the most frequently occurring measurement(Johnson, 1996). The mean, or the average is also included in this univariate analysis, as well as the median, which  basically cuts the distribution of answers exactly in half. Half of the answers were greater than the number and half the answers were less than the number given as the