Introduction to Statistics

2018

Critical care (London, England), 2002

The previous review in this series introduced the notion of data description and outlined some of the more common summary measures used to describe a dataset. However, a dataset is typically only of interest for the information it provides regarding the population from which it was drawn. The present review focuses on estimation of population values from a sample.

Statistics is concerned with scientific methods for collecting, organizing, summarizing, Presenting and analyzing data as well as deriving valid conclusions and making reasonable Decisions on the basis of this analysis. Statistics is concerned with the systematic collection of Numerical data and its interpretation.

2003

This evaluation reports the results of research and analysis undertaken by the U.S. Census

2014

and demographic data. b (population age 0-14 + 65 and above) / (population age 15-64) c (population age 65 and above) / (population age 15-64) Source: KSH.

Methods of Demographic Analysis, 2013

The purpose of this chapter is to introduce some basic statistical measures that are commonly used in demographic analysis. The concepts are defined in general terms without going into theoretical details. Methods of calculation of various measures are described. The statistical measures discussed in this chapter consist of counts, frequencies, proportions, rates, various measures of central tendency, dispersion, comparison, correlation and regression. 3.2 Demographic Data and Analysis Demographic data can be classified according to their level of measurement. This is useful because the level of measurement helps the selection of what statistical analysis is most appropriate. Several classifications can be used. This book uses the four-fold classification system proposed by Stevens (1946) that classifies data as being (1) nominal, (2) ordinal, (3) interval and (4) ratio. There are other systems such as the twofold classification of (1) discrete and (2) continuous. In the classification system proposed by Stevens, the nominal level is known as the lowest level of measurement. Here, the values just name the attribute uniquely and do not imply an ordering of cases. For example, the variable marital is inherently nominal. In a study it might be useful to have attributes such as never married, married, separated, divorced and widowed. These attributes are mutually exclusive and exhaustive. They could be coded N, M, S, D and W respectively, or coded as 1, 2, 3, 4 and 5. In the latter, the numbers are not numbers in a real sense since they cannot be added or subtracted. Thus, numbers assigned to serve as values for nominal level variables such as marital status cannot be added, subtracted, multiplied or divided in a meaningful way. An exception is the dummy coding of F. Yusuf et al., Methods of Demographic Analysis,

2012

The very expression Gender Statistics calls for a double interpretation. It accounts for the popular mix-up of statistical methodology with its typical products such as indexes, tables and graphs. At the same time it implies a broader and forward-looking perspective, which is inspired by the increasing demand of gender sensitive statistical information coming from society, official agencies, economy. Gender statistics stands as a proper independent field of statistics with its own objectives and a variety of applications in social, human and life science. Concurrently an emerging necessity of appropriate equipment of methods and dissemination tools is noticeable. The paper tracks the roots and the historical development of gender statistics, reviews critically the existing indexes and practice and outlines methodological needs and research prospects.