Hazard Function: In medical statistics, the hazard function is a relationship between a proportion and time. The proportion (also called the hazard ratio) is the proportion of subjects who die in an increment of time starting at time "t" from among those who have survived to time "t." The term...

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Hazard Rate: See Hazard function Browse Other Glossary Entries

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Heteroscedasticity: Heteroscedasticity generally means unequal variation of data, e.g. unequal variance . For special cases see heteroscedasticity in regression , heteroscedasticity in hypothesis testing See also: homoscedasticity Browse Other Glossary Entries

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Heteroscedasticity in hypothesis testing: In hypothesis testing , heteroscedasticity means a situation in which the variance is different for compared samples. Heteroscedasticity complicates testing because most tests rest on the assumption of equal variance. See also: homoscedasticity in hypothesis testing Browse Other Glossary Entries

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Heteroscedasticity in regression: In regression analysis , heteroscedasticity means a situation in which the variance of the dependent variable varies across the data. Heteroscedasticity complicates analysis because many methods in regression analysis are based on an assumption of equal variance. See also: homoscedasticity in regression , Browse Other Glossary Entries

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Histogram: A histogram is a graph of a dataset, composed of a series of rectangles. The width of these rectangles is proportional to the range of values in a class or bin, all bins being the same width. For example, values lying between 1 and 3, between 3 and 5,...

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Homoscedasticity: Homoscedasticity generally means equal variation of data, e.g. equal variance . For special cases see homoscedasticity in regression , homoscedasticity in hypothesis testing See also: heteroscedasticity Browse Other Glossary Entries

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Statistical Glossary Homoscedasticity in hypothesis testing: In hypothesis testing , homoscedasticity means a situation in which the variance is the same for all the compared samples. Homoscedasticity facilitates testing because most tests rest on the assumption of equal variance. See also: heteroscedasticity , heteroscedasticity in hypothesis testing Browse Other Glossary...

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Homoscedasticity in regression: In regression analysis , homoscedasticity means a situation in which the variance of the dependent variable is the same for all the data. Homoscedasticity is facilitates analysis because most methods are based on the assumption of equal variance. See also: heteroscedasticity in regression Browse Other Glossary Entries

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Independent Events: Two events A and B are said to be independent if P(AB) = P(A).P(B). To put it differently, events A and B are independent if the occurrence or non-occurrence of A does not influence the occurrence of non-occurrence of B and vice-versa. For example, if I toss a...

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Independent Random Variables: Two or more random variables are said to be independent it their joint distribution (density) is the product of their marginal distributions (densities). Browse Other Glossary Entries

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Indicator: See manifest variable Browse Other Glossary Entries

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Inferential Statistics: Inferential statistics is the body of statistical techniques that deal with the question "How reliable is the conclusion or estimate that we derive from a set of data?" The two main techniques are confidence intervals and hypothesis tests. Browse Other Glossary Entries

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Interval Scale: An interval scale is a measurement scale in which a certain distance along the scale means the same thing no matter where on the scale you are, but where "0" on the scale does not represent the absence of the thing being measured. Fahrenheit and Celsius temperature scales...

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Jackknife: The jackknife is a general non-parametric method for estimation of the bias and variance of a statistic (which is usually an estimator) using only the sample itself. The jackknife is considered as the predecessor of the bootstrapping techniques. With a sample of size N, the jackknife involves calculating N...

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Joint Probability Density: A function f(x,y) is called the joint probability density of random variables X and Y if and only if for any region A on the xy-plane Browse Other Glossary Entries

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Joint Probability Distribution: If X and Y are discrete random variables, the function f(x,y) which gives the probability that X = x and Y = y for each pair of values (x,y) within the range of values of X and Y is called the joint probability distribution of X and...

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Latent Variable: A latent variable describes an unobservable construct and cannot be observed or measured directly. Latent variables are essential elements of latent variable models . A latent variable can be categorical or continuous. The opposite concept is the manifest variable . Browse Other Glossary Entries

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Level of a Factor: In design of experiments, levels of a factor are the values it takes on. The values are not necessarily numbers - they may be at a nominal scale, ordinal scale, etc. See Variables (in design of experiments) for an explanatory example. Browse Other Glossary Entries

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Likelihood Function: Likelihood function is a fundamental concept in statistical inference. It indicates how likely a particular population is to produce an observed sample. Let P(X; T) be the distribution of a random vector X, where T is the vector of parameters of the distribution. If Xo is the observed...

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