Statistics 2: generalized linear models
... assumed by the variance function -> can be taken care with dispersal parameter • This can also occur with binomial data ...
... assumed by the variance function -> can be taken care with dispersal parameter • This can also occur with binomial data ...
Review of Chapters 9-11 - UF-Stat
... • The linear regression equation E(y) = a + b x is part of a model. The model has another parameter σ that describes the variability of the conditional distributions; that is, the variability of y values for all subjects having the same x-value. • For an observation, difference y yˆ between obser ...
... • The linear regression equation E(y) = a + b x is part of a model. The model has another parameter σ that describes the variability of the conditional distributions; that is, the variability of y values for all subjects having the same x-value. • For an observation, difference y yˆ between obser ...
Ch 8 Notes
... • State the following: As the ________ (explanatory variable) increases by 1 _______ (insert unit) the __________ (response variable) is predicted to increase/decrease (use appropriate word given sign of slope) by _______ (insert slope here and units). • As the caloric content of a burger increases ...
... • State the following: As the ________ (explanatory variable) increases by 1 _______ (insert unit) the __________ (response variable) is predicted to increase/decrease (use appropriate word given sign of slope) by _______ (insert slope here and units). • As the caloric content of a burger increases ...
SEM details (chapter 6) - Bill Shipley recherche
... asymptotically unbiased. What does this mean? The calculated probabilities are only exact as sample size reaches infinity!!! In practice, the minimum sample size needed for good probability estimates depends on the ratio of observations to free parameters that have to be estimated. If the data are n ...
... asymptotically unbiased. What does this mean? The calculated probabilities are only exact as sample size reaches infinity!!! In practice, the minimum sample size needed for good probability estimates depends on the ratio of observations to free parameters that have to be estimated. If the data are n ...
Vocabulary 2
... Ethics ! • ethic (n): a moral principle or a set of principles • ethical (a): dealing with moral questions or theory • ethics (n, pl): the science of morals; a system of principles… (Source: The Cassell Concise Dictionary) ...
... Ethics ! • ethic (n): a moral principle or a set of principles • ethical (a): dealing with moral questions or theory • ethics (n, pl): the science of morals; a system of principles… (Source: The Cassell Concise Dictionary) ...
simulation study
... The location problem in Exercise 8.13 describes the estimator µ̃ that minimizes i |Xi − µ|3/2 as intermediate between L2 (least squares) and L1 (least absolute value). For simple linear regression, pushing in the other direction leads to L∞ , minimizing maxi |Yi − β0 − β1 xi |. Compare estimators ba ...
... The location problem in Exercise 8.13 describes the estimator µ̃ that minimizes i |Xi − µ|3/2 as intermediate between L2 (least squares) and L1 (least absolute value). For simple linear regression, pushing in the other direction leads to L∞ , minimizing maxi |Yi − β0 − β1 xi |. Compare estimators ba ...
notes
... For example, 10 more years of education increases the probability of being in the labor force by 0.038(10) = 0.38 ...
... For example, 10 more years of education increases the probability of being in the labor force by 0.038(10) = 0.38 ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.