Free reference · 596 entries
The words, before the course.
Short, plain definitions of the terms that turn up in statistics and data science — what they mean, and how they relate to each other. Nothing to sign in for.
40 of 596 entries under L
- LabelA label is a category into which a record falls, usually in the context of predictive modeling.
- Lan-Demets Spending FunctionSee alpha spending function.
- Latent Structure ModelsLatent structure models is a generic term for a broad set of categories of statistical models.
- Latent VariableA latent variable describes an unobservable construct and cannot be observed or measured directly.
- Latent Variable Growth Curve ModelsThese techniques, also called Latent Curve Models (LCM), take traditional modeling of growth curves for repeated measures data and extend it to cover the use of latent variables.
- Latent Variable ModelsLatent variable models are a broad subclass of latent structure models. They postulate some relationship between the statistical properties of observable variables (or “manifest variables”, or “indicators”)…
- Latin SquareThe Latin Square is a square array in which every letter or symbol appears exactly one in each row and in each column.
- Law Of Large NumbersAccording to the Law of Large Numbers, the probability that the proportion of successes in a sample will differ from the population proportion by less than c ( any positive constant) approaches 1 as the…
- Lawley-Hotelling TraceSee Hotelling Trace coefficient.
- Least Squares MethodIn a narrow sense, the Least Squares Method is a technique for fitting a straight line through a set of points in such a way that the sum of the squared vertical distances from the observed points to the…
- Level of a FactorIn 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.
- Level Of SignificanceIn hypothesis testing, you seek to decide whether observed results are consistent with chance variation under the “null hypothesis,” or, alternatively, whether they are so different that chance variability…
- Life TablesIn survival analysis, life tables summarize lifetime data or, generally speaking, time-to-event data.
- Likelihood FunctionLikelihood function is a fundamental concept in statistical inference. It indicates how likely a particular population is to produce an observed sample.
- Likelihood Function (Graphical)Likelihood function is a fundamental concept in statistical inference. It indicates how likely a particular population is to produce an observed sample.
- Likelihood Ratio TestThe likelihood ratio test is aimed at testing a simple null hypothesis against a simple alternative hypothesis.
- Likelihood Ratio Test (Graphical)The likelihood ratio test is aimed at testing a simple null hypothesis against a simple alternative hypothesis.
- Likert ScalesLikert scales are categorical ordinal scales used in social sciences to measure attitude. Measurements at Likert scales usually take on an odd number of values with a middle point, e.g.
- Lilliefors StatisticThe Lilliefors statistic is used in a goodness-of-fit test of whether an observed sample distribution is consistent with normality.
- Lilliefors test for normalityThe Lilliefors test is a special case of the Kolmogorov-Smirnov goodness-of-fit test. In the Lilliefors test, the Kolmogorov-Smirnov test is implemented using the sample mean and standard deviation as the…
- Line of RegressionThe line of regression is the line that best fits the data in simple linear regression, i.e.
- Linear FilterA linear filter is the filter whose output is a linear function of the input. Any output value of a linear filter is the weighted mean of input values.
- Linear ModelA linear model specifies a linear relationship between a dependent variable and n independent variables: y = a0 + a1 x1 + a2 x2 + ¼+ an xn, where y is the dependent variable, {xi} are independent…
- Linear Model (Graphical)A linear model specifies a linear relationship between a dependent variable and n independent variables: where y is the dependent variable, {xi} are independent variables, {ai} are parameters of the model.
- Linear RegressionLinear regression is aimed at finding the “best-fit” linear relationship between the dependent variable and independent variable(s).
- Linkage FunctionA linkage function is an essential prerequisite for hierarchical cluster analysis. Its value is a measure of the “distance” between two groups of objects (i.e.
- Local IndependenceThe local independence postulate plays a central role in latent variable models. Local independence means that all the manifest variables are independent random variables if the latent variables are…
- Log-log PlotA log-log plot represents observed units described by two variables, say x and y , as a scatter graph.
- Log-Normal DistributionA random variable X has a log-normal distribution if ln(X) is normally distributed.
- Logistic Regression1–pi Li = log pi = a + b xi, where pi is the probability of a success for given value xi of the explanatory variable X.
- Logistic Regression (Graphical)Logistic regression is used with binary data when you want to model the probability that a specified outcome will occur.
- Logit(1 – p) logit(p) = log p Logit is widely used to construct statistical models, for example in logistic regression.
- Logit and Odds RatioThe following relation between the odds ratio and logit is often used for constructing statistical models: log OR(p1, p2) = logit (p1) – logit (p2) where p1, p2 are probabilities, OR (p1, p2) is the odds…
- Logit ModelsLogit models postulate some relation between the logit of observed probabilities (not the probabilities themselves), and unknown parameters of the model.
- Loglinear modelsLoglinear models are models that postulate a linear relationship between the independent variables and the logarithm of the dependent variable, for example: log(y) = a0 + a1 x1 + a2 x2 … + aN xN where y is…
- Loglinear regressionLoglinear regression is a kind of regression aimed at finding the best fit between the data and a loglinear model.
- Longitudinal AnalysisLongitudinal analysis is concerned with statistical inference from longitudinal data
- Longitudinal DataLongitudinal data refer to observations of given units made over time. A simple example of longitudinal data is the gross annual income of, say, 1000 households from New York City for the years 1991-2000.
- Longitudinal studyLongitudinal studies are those that record data for subjects or variables over time. If a longitudinal study uses the same subjects at each point where data are recorded, it is a panel study.
- Loss FunctionA loss function specifies a penalty for an incorrect estimate from a statistical model. Typical loss functions might specify the penalty as a function of the difference between the estimate and the true…
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