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Unit-weighted Regression
In statistics, unit-weighted regression is a simplified and robust version ( Wainer & Thissen, 1976) of multiple regression analysis where only the intercept term is estimated. That is, it fits a model :\hat = \hat(\mathbf) = \hat + \sum_i x_i where each of the x_i are binary variables, perhaps multiplied with an arbitrary weight. Contrast this with the more common multiple regression model, where each predictor has its own estimated coefficient: :\hat = \hat(\mathbf) = \hat + \sum_i \hat_i x_i In the social sciences, unit-weighted regression is sometimes used for binary classification, i.e. to predict a yes-no answer where \hat < 0 indicates "no", "yes". It is easier to interpret than multiple linear regression (known as in the classification case). Unit ...
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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] |
Standard Score
In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured. Raw scores above the mean have positive standard scores, while those below the mean have negative standard scores. It is calculated by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This process of converting a raw score into a standard score is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization for more). Standard scores are most commonly called ''z''-scores; the two terms may be used interchangeably, as they are in this article. Other equivalent terms in use include z-values, normal scores, standardized variables and pull in high energy physics. Computing a z-score requires knowledge of the mean and standard dev ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Psychological Bulletin
The ''Psychological Bulletin'' is a monthly peer-reviewed academic journal that publishes evaluative and integrative research reviews and interpretations of issues in psychology, including both qualitative (narrative) and/or quantitative (meta-analytic) aspects. The editor-in-chief is Dolores Albarracín (University of Illinois at Urbana–Champaign). History The journal was established by Johns Hopkins psychologist James Mark Baldwin in 1904,Benjamin, Ludy T. ''A Brief History of Modern Psychology''. Malden, MA: Blackwell Pub., 2007, pp. 70–1, . immediately after he had bought out James McKeen Cattell's share of ''Psychological Review'', which the two had established ten years earlier. Baldwin gave the editorship of both journals to John B. Watson, when scandal forced him to resign his position at Johns Hopkins in 1920. Ownership of the ''Bulletin'' passed to Howard C. Warren, who eventually donated it to the American Psychological Association, which continues to own it to the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Journal Of Educational And Behavioral Statistics
The ''Journal of Educational and Behavioral Statistics'' is a peer-reviewed academic journal published by SAGE Publications on behalf of the American Educational Research Association and American Statistical Association. It covers statistical methods and applied statistics in the Education studies, educational and behavioral sciences. The journal was established in 1976 as the ''Journal of Educational Statistics'' and obtained its current name in 1994. The journal's Editor-in-chief, editor is Steven Andrew Culpepper. Mission Statement The ''Journal of Educational and Behavioral Statistics'' (''JEBS'') provides an outlet for papers that are original and useful to those applying statistical approaches to problems and issues in educational or behavioral research. Typical papers will present new methods of analysis. In addition, critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods will be published. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robust Regression
In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations). Robust regression methods are designed to limit the effect that violations of assumptions by the underlying data-generating process have on regression estimates. For example, least squares estimates for regression models are highly sensitive to outliers: an outlier with twice the error magnitude of a typical observation contributes four (two squared) times as much to the squared error loss, and therefore has more leverage over the regression estimates. The Huber loss function is a robust alternative to standard square error loss that reduces ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regression Analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared differences between the true data and that line (or hyperplane). For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear Regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called '' simple linear regression''; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Proper Linear Model
In statistics, a proper linear model is a linear regression model in which the weights given to the predictor variables are chosen in such a way as to optimize the relationship between the prediction and the criterion. Simple regression analysis is the most common example of a proper linear model. Unit-weighted regression In statistics, unit-weighted regression is a simplified and robust version ( Wainer & Thissen, 1976) of multiple regression analysis where only the intercept term is estimated. That is, it fits a model :\hat = \hat(\mathbf) = \hat + \sum_i x_i wh ... is the most common example of an improper linear model. Bibliography * Regression models {{Statistics-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cross-validation (statistics)
Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of ''known data'' on which training is run (''training dataset''), and a dataset of ''unknown data'' (or ''first seen'' data) against which the model is tested (called the validation dataset or ''testing set''). The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jacob Cohen (statistician)
Jacob Cohen (April 20, 1923 – January 20, 1998) was an American psychologist and statistician best known for his work on statistical power and effect size, which helped to lay foundations for current statistical meta-analysis and the methods of estimation statistics. He gave his name to such measures as Cohen's kappa, Cohen's ''d'', and Cohen's ''h''. Power analysis and significance testing In addition to being an advocate of power analysis and effect size, Cohen was a critic of reliance on, and lack of understanding of, significance testing procedures used in statistics, especially misunderstandings of null hypothesis significance testing. In particular, he identified the "near universal misinterpretation of p as the probability that H0 is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects H0 one thereby affirms the theory that led to the test". He encouraged instead a recognition ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robyn Dawes
Robyn Mason Dawes (July 23, 1936 – December 14, 2010) was an American psychologist who specialized in the field of human judgment. His research interests included human irrationality, human cooperation, intuitive expertise, and the United States AIDS policy. He applied linear models to human decision making, including models with equal weights, a method known as unit-weighted regression. He co-wrote an early textbook on mathematical psychology (see below). Early life and education Dawes earned his B.A. in Philosophy at Harvard (1958) and his Master’s in Clinical Psychology (1960) at the University of Michigan before earning his Doctorate in Mathematical Psychology (1963) at the same institution. Career Dawes held jobs at the University of Oregon, where he served as Department Head for five years, as well as the Oregon Research Institute. In 1985, Dawes joined the Department of Social and Decision Sciences (SDS) at Carnegie Mellon University where he served as Department ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multivariate Analysis
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied. In addition, multivariate statistics is concerned with multivariate probability distributions, in terms of both :*how these can be used to represent the distributions of observed data; :*how they can be used as part of statistical inference, particularly where several different quantities are of interest to the same analysis. Certain types of problems involving multivariate data, for example simple linear regression an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
