|
Ecological Correlation
In statistics, an ecological correlation (also ''spatial correlation'') is a correlation between two variables that are group means, in contrast to a correlation between two variables that describe individuals. For example, one might study the correlation between physical activity and weight among sixth-grade children. A study at the individual level might make use of 100 children, then measure both physical activity and weight; the correlation between the two variables would be at the individual level. By contrast, another study might make use of 100 classes of sixth-grade students, then measure the mean physical activity and the mean weight of each of the 100 classes. A correlation between these group means would be an example of an ecological correlation. Because a correlation describes the measured strength of a relationship, correlations at the group level can be much higher than those at the individual level. Thinking both are equal is an example of ecological fallacy. See al ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German language, German: ''wikt:Statistik#German, Statistik'', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of statistical survey, surveys and experimental design, experiments.Dodge, Y. (2006) ''The Oxford Dictionary of Statistical Terms'', Oxford University Press. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey sample (statistics), samples. Representative sampling as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Correlation
In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are ''linearly'' related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mean
There are several kinds of mean in mathematics, especially in statistics. Each mean serves to summarize a given group of data, often to better understand the overall value (magnitude and sign) of a given data set. For a data set, the ''arithmetic mean'', also known as "arithmetic average", is a measure of central tendency of a finite set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers ''x''1, ''x''2, ..., x''n'' is typically denoted using an overhead bar, \bar. If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is the ''sample mean'' (\bar) to distinguish it from the mean, or expected value, of the underlying distribution, the ''population mean'' (denoted \mu or \mu_x).Underhill, L.G.; Bradfield d. (1998) ''Introstat'', Juta and Company Ltd.p. 181/ref> Outside probability and statistics, a wide range of other notions of mean are o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
American Sociological Review
The ''American Sociological Review'' is a bi-monthly peer-reviewed academic journal covering all aspects of sociology. It is published by SAGE Publications on behalf of the American Sociological Association. It was established in 1936. The editors-in-chief are Arthur S. Alderson (Indiana University-Bloomington) and Dina G. Okamoto (Indiana University-Bloomington). History For its first thirty years, the American Sociological Society (now the American Sociological Association) was largely dominated by the sociology department of the University of Chicago, and the quasi-official journal of the association was Chicago's '' American Journal of Sociology''. In 1935, the executive committee of the American Sociological Society voted 5 to 4 against disestablishing the ''American Journal of Sociology'' as the official journal of society, but the measure was passed on for consideration of the general membership, which voted 2 to 1 to establish a new journal independent of Chicago: the ''Amer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ecological Fallacy
An ecological fallacy (also ecological ''inference'' fallacy or population fallacy) is a formal fallacy in the interpretation of statistical data that occurs when inferences about the nature of individuals are deduced from inferences about the group to which those individuals belong. "Ecological fallacy" is a term that is sometimes used to describe the fallacy of division, which is not a statistical fallacy. The four common statistical ecological fallacies are: confusion between ecological correlations and individual correlations, confusion between group average and total average, Simpson's paradox, and confusion between higher average and higher likelihood. Examples Mean and median An example of ecological fallacy is the assumption that a population mean has a simple interpretation when considering likelihoods for an individual. For instance, if the mean score of a group is larger than zero, this does not imply that a random individual of that group is more likely to have ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ecological Regression
Ecological regression is a statistical technique which runs regression on aggregates, often used in political science and history to estimate group voting behavior from aggregate data. For example, if counties have a known Democratic vote (in percentage) D, and a known percentage of Catholics, C, then running a linear regression of dependent variable D against independent variable C will give D = a + bC. If the regression gives D = .22 + .45C for example, then the estimated Catholic vote (C = 1) is 67% Democratic and the non-Catholic vote (C = 0) is 22% Democratic. The technique has been often used in litigation brought under the Voting Rights Act of 1965 to see how blacks and whites voted. See also *Ecological correlation *Ecological fallacy An ecological fallacy (also ecological ''inference'' fallacy or population fallacy) is a formal fallacy in the interpretation of statistical data that occurs when inferences about the nature of individuals are deduced from inferences about ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Geographic Information Science
Geographic information science or geographical information science (GIScience or GISc) is the scientific discipline that studies geographic information, including how it represents phenomena in the real world, how it represents the way humans understand the world, and how it can be captured, organized, and analyzed. It can be contrasted with geographic information systems (GIS), which are the actual repositories of such data, the software tools for carrying out relevant tasks, and the profession of GIS users. That said, one of the major goals of GIScience is to find practical ways to improve GIS data, software, and professional practice. it is more focused on how gis is applied in real life British geographer Michael Goodchild defined this area in the 1990s and summarized its core interests, including spatial analysis, visualization, and the representation of uncertainty. GIScience is conceptually related to geomatics, information science, computer science, but it claims the sta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spatial Autocorrelation
Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data or transcriptomics data. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. The most fundamental of these is the problem of defining the spatial location of the entities being studied. Classification of the techniques of spatia ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Complete Spatial Randomness
Complete spatial randomness (CSR) describes a point process whereby point events occur within a given study area in a completely random fashion. It is synonymous with a ''homogeneous spatial Poisson process''.O. Maimon, L. Rokach, ''Data Mining and Knowledge Discovery Handbook'' , Second Edition, Springer 2010, pages 851-852 Such a process is modeled using only one parameter \rho, i.e. the density of points within the defined area. The term complete spatial randomness is commonly used in Applied Statistics in the context of examining certain point patterns, whereas in most other statistical contexts it is referred to the concept of a spatial Poisson process.O. Maimon, L. Rokach, ''Data Mining and Knowledge Discovery Handbook'' , Second Edition, Springer 2010, pages 851-852 Model Data in the form of a set of points, irregularly distributed within a region of space, arise in many different contexts; examples include locations of trees in a forest, of nests of birds, of nuclei in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Modifiable Areal Unit Problem
__NOTOC__ The modifiable areal unit problem (MAUP) is a source of statistical bias that can significantly impact the results of statistical hypothesis tests. MAUP affects results when point-based measures of spatial phenomena are aggregated into districts, for example, population density or illness rates. The resulting summary values (e.g., totals, rates, proportions, densities) are influenced by both the shape and scale of the aggregation unit. For example, census data may be aggregated into county districts, census tracts, postcode areas, police precincts, or any other arbitrary spatial partition. Thus the results of data aggregation are dependent on the mapmaker's choice of which "modifiable areal unit" to use in their analysis. A census choropleth map calculating population density using state boundaries will yield radically different results than a map that calculates density based on county boundaries. Furthermore, census district boundaries are also subject to change over ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spatial Epidemiology
Spatial epidemiology is a subfield of epidemiology focused on the study of the spatial distribution of health outcomes; it is closely related to health geography. Specifically, spatial epidemiology is concerned with the description and examination of disease and its geographic variations. This is done in consideration of “demographic, environmental, behavioral, socioeconomic, genetic, and infections risk factors." Types of studies ;Disease Mapping: * Disease maps are visual representations of intricate geographic data that provide a quick overview of said information. Mainly used for explanatory purposes, disease maps can be presented to survey high-risk areas and to help policy and resource allocation in said areas. ;Geographic correlation studies * Geographic correlation studies attempt to study the geographical factors and their effects on geographically differentiated health outcomes. Measured on an ecologic scale, these factors include environmental variables (quality of s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spatial Econometrics
Spatial econometrics is the field where spatial analysis and econometrics intersect. The term “spatial econometrics” was introduced for the first time by the Belgian economist Jean Paelinck (universally recognised as the father of the discipline) in the general address he delivered to the annual meeting of the Dutch Statistical Association in May 1974 (Paelinck and Klaassen, 1979). In general, econometrics differs from other branches of statistics in focusing on theoretical models, whose parameters are estimated using regression analysis. Spatial econometrics is a refinement of this, where either the theoretical model involves interactions between different entities, or the data observations are not truly independent. Thus, models incorporating spatial auto-correlation or neighborhood effects can be estimated using spatial econometric methods. Such models are common in regional science, real estate economics, education economics, housing market and many others. Adopting a more ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
