36 Sentences With "outcome variable" | Random Sentence Generator

Those questions essentially ask what the model should predict (the outcome variable).

"It's perfectly reasonable to get really excited and to try to extrapolate from mid-midterm elections because it gives us an outcome variable," he said.

Classification of occupations 1966. London, UK7 HMSO. A correlation and structural equation model analysis was conducted. In the structural equation models, social status in the 1970s was the main outcome variable.

At least one of the products on the right side of the above equation must not equal 0 (i.e. either β53 ≠ 0 and β64 ≠ 0, or β65 ≠ 0 and β51 ≠ 0). As well, since there is no overall moderation of the treatment effect of A on the outcome variable C (β43 = 0), this means that β63 cannot equal 0. In other words, the residual direct effect of A on the outcome variable C, controlling for the mediator, is moderated.

Another way is Supervised or Unsupervised Analysis. Supervised Analysis uses an outcome variable to be able to create prediction models. Unsupervised Analysis summarizes the information we have and can be represented graphically. So that the conclusion of our results is clearly visible.

Psychological Review, 103, 650–669. Search rule: Look through cues in the order of their validity. Stopping rule: Stop search when the first cue is found where the values of the two alternatives differ. Decision rule: Predict that the alternative with the higher cue value has the higher value on the outcome variable.

STIR reformulates and slightly adjusts the original Relief formula by incorporating sample variance of the nearest neighbor distances into the attribute importance estimation. This variance permits the calculation of statistical significance of features and adjustment for multiple testing of Relief-based scores. Currently, STIR supports binary outcome variable but will soon be extended to multi-state and continuous outcome.

Suppression can occur when a single causal variable is related to an outcome variable through two separate mediator variables, and when one of those mediated effects is positive and one is negative. In such a case, each mediator variable suppresses or conceals the effect that is carried through the other mediator variable. For example, higher intelligence scores (a causal variable, A) may cause an increase in error detection (a mediator variable, B) which in turn may cause a decrease in errors made at work on an assembly line (an outcome variable, X); at the same time, intelligence could also cause an increase in boredom (C), which in turn may cause an increase in errors (X). Thus, in one causal path intelligence decreases errors, and in the other it increases them.

Moderated mediation relies on the same underlying models (specified above) as mediated moderation. The main difference between the two processes is whether there is overall moderation of the treatment effect of A on the outcome variable C. If there is, then there is mediated moderation. If there is no overall moderation of A on C, then there is moderated mediation.

Within groups, the evaluators averaged students' scores on each measure (or outcome variable) in order to yield a "group" score. Thus, the group scores of FT students were compared to the group scores of NFT students. An important—and later controversial—statistical adjustment technique was employed by the evaluators in order to "improve the integrity of the results". Because there were differences between treatment and comparison groups (e.g.

The causal effect of a treatment on a single unit at a point in time is the difference between the outcome variable with the treatment and without the treatment. The Fundamental Problem of Causal Inference is that it is impossible to observe the causal effect on a single unit. You either take the aspirin now or you don't. As a consequence, assumptions must be made in order to estimate the missing counterfactuals.

Concurrent validity differs from convergent validity in that it focuses on the power of the focal test to predict outcomes on another test or some outcome variable. Convergent validity refers to the observation of strong correlations between two tests that are assumed to measure the same construct. It is the interpretation of the focal test as a predictor that differentiates this type of evidence from convergent validity, though both methods rely on simple correlations in the statistical analysis.

In sports psychology, physical self-concept is considered both a valued outcome variable and a mediating variable that facilitates the attainment of other valued outcomes like physical skills, health-related physical fitness, physical activity, exercise adherence in non- elite settings, and improved performance in elite sports. Marsh, Morin, and Parker (2015) demonstrated that the BFLPE exists with regard to physical concept. The study showed that class-average physical ability in physical fitness was negatively associated with physical self-concept.

In statistical inference based on regression coefficients, yes; in predictive modelling applications, correction is neither necessary nor appropriate. To understand this, consider the measurement error as follows. Let y be the outcome variable, x be the true predictor variable, and w be an approximate observation of x. Frost and Thompson suggest, for example, that x may be the true, long-term blood pressure of a patient, and w may be the blood pressure observed on one particular clinic visit.

In one published example of an application of binomial regression,Cox & Snell (1981), Example H, p. 91 the details were as follows. The observed outcome variable was whether or not a fault occurred in an industrial process. There were two explanatory variables: the first was a simple two-case factor representing whether or not a modified version of the process was used and the second was an ordinary quantitative variable measuring the purity of the material being supplied for the process.

Cumulative Distribution Function of Hyperbolastic Type I, Logistic, and Hyperbolastic Type II PDF of H1, Logistic, and H2 Hyperbolastic regressions are statistical models that utilize standard hyperbolatic functions to model a dichotomous outcome variable. The purpose of binary regression is to predict a binary outcome (dependent) variable using a set of explanatory (independent) variables. Binary regression is routinely used in many areas including medical, public health, dental, and biomedical sciences. Binary regression analysis was used to predict endoscopic lesions in iron deficiency anemia.

From the Economics community, the independent variables are also called exogenous. Depending on the context, a dependent variable is sometimes called a "response variable", "regressand", "criterion", "predicted variable", "measured variable", "explained variable", "experimental variable", "responding variable", "outcome variable", "output variable", "target" or "label".. In economics endogenous variables are usually referencing the target. "Explanatory variable" is preferred by some authors over "independent variable" when the quantities treated as independent variables may not be statistically independent or independently manipulable by the researcher.Everitt, B.S. (2002) Cambridge Dictionary of Statistics, CUP.

MPE started as analysis of risk factors (such as smoking) and molecular pathologic findings (such as KRAS oncogene mutation in lung cancer). Studies to examine the relationship between an exposure and molecular pathologic signature of disease (particularly, cancer) became increasingly common throughout the 1990s and early 2000s. The use of molecular pathology in epidemiology posed lacked standardized methodologies and guidelines as well as interdisciplinary experts and training programs. MPE research required a new conceptual framework and methodologies (epidemiological method) because MPE examines heterogeneity in an outcome variable.

Predictive mean matching (PMM) is a widely used statistical imputation method for missing values, first proposed by Donald B. Rubin in 1986 and R. J. A. Little in 1988. It aims to reduce the bias introduced in a dataset through imputation, by drawing real values sampled from the data. This is achieved by building a small subset of observations where the outcome variable matches the outcome of the observations with missing values. Compared to other imputation methods, it usually imputes less implausible values (e.g.

Moderated mediation, also known as conditional indirect effects,Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007) Addressing moderated mediation hypotheses: Theory, Methods, and Prescriptions. Multivariate Behavioral Research, 42, 185–227. occurs when the treatment effect of an independent variable A on an outcome variable C via a mediator variable B differs depending on levels of a moderator variable D. Specifically, either the effect of A on the B, and/or the effect of B on C depends on the level of D.

Consider fitting a straight line for the relationship of an outcome variable y to a predictor variable x, and estimating the slope of the line. Statistical variability, measurement error or random noise in the y variable causes uncertainty in the estimated slope, but not bias: on average, the procedure calculates the right slope. However, variability, measurement error or random noise in the x variable causes bias in the estimated slope (as well as imprecision). The greater the variance in the x measurement, the closer the estimated slope must approach zero instead of the true value.

Also, research on the topic is not particularly helpful in describing the specific leader behaviors that promote high quality relationships; in fact, these behaviors are exogenous to LMX, which is an outcome variable (i.e., trusting, liking, etc.). Thus, exogenous manipulation of the construct is not possible and only manipulation of its antecedents is possible. This is due largely to the fact that LMX is a descriptive (rather than normative) theory which focuses on explaining how people relate to and interact with each other rather than on a prescription for how to form high quality LMX relationships.

Difference in differences (DID or DD) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. It calculates the effect of a treatment (i.e., an explanatory variable or an independent variable) on an outcome (i.e., a response variable or dependent variable) by comparing the average change over time in the outcome variable for the treatment group, compared to the average change over time for the control group.

Although it is intended to mitigate the effects of extraneous factors and selection bias, depending on how the treatment group is chosen, this method may still be subject to certain biases (e.g., mean regression, reverse causality and omitted variable bias). In contrast to a time-series estimate of the treatment effect on subjects (which analyzes differences over time) or a cross-section estimate of the treatment effect (which measures the difference between treatment and control groups), difference in differences uses panel data to measure the differences, between the treatment and control group, of the changes in the outcome variable that occur over time.

A principal danger of such data redundancy is that of overfitting in regression analysis models. The best regression models are those in which the predictor variables each correlate highly with the dependent (outcome) variable but correlate at most only minimally with each other. Such a model is often called "low noise" and will be statistically robust (that is, it will predict reliably across numerous samples of variable sets drawn from the same statistical population). So long as the underlying specification is correct, multicollinearity does not actually bias results; it just produces large standard errors in the related independent variables.

Cambridge University Press, Cambridge. In a Rubin causal model potential outcomes framework, where Y1 is the outcome variable of people for who the participation indicator D equals 1, the control function approach leads to the following model as long as the potential outcomes Y0 and Y1 are independent of D conditional on X and Z.Heckman, J. J., and E. J. Vytlacil (2007): Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast the Effects in New Environments. Handbook of Econometrics, Vol 6, ed. by J. J. Heckman and E. E. Leamer.

Regression discontinuity design requires that all potentially relevant variables besides the treatment variable and outcome variable be continuous at the point where the treatment and outcome discontinuities occur. One sufficient, though not necessary , condition is if the treatment assignment is "as good as random" at the threshold for treatment. If this holds, then it guarantees that those who just barely received treatment are comparable to those who just barely did not receive treatment, as treatment status is effectively random. Treatment assignment at the threshold can be "as good as random" if there is randomness in the assignment variable and the agents considered (individuals, firms, etc.) cannot perfectly manipulate their treatment status.

Regression line for 50 random points in a Gaussian distribution around the line y=1.5x+2 (not shown). In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is linear regression, in which a researcher 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 distances between the true data and that line (or hyperplane).

Suppose the green and blue data points capture the same data, but with errors (either +1 or -1 on x-axis) for the green points. Minimizing error on the y-axis leads to a smaller slope for the green points, even if they are just a noisy version of the same data. It may seem counter-intuitive that noise in the predictor variable x induces a bias, but noise in the outcome variable y does not. Recall that linear regression is not symmetric: the line of best fit for predicting y from x (the usual linear regression) is not the same as the line of best fit for predicting x from y.

Suppose the change in x is known under some new circumstance: to estimate the likely change in an outcome variable y, the slope of the regression of y on x is needed, not y on w. This arises in epidemiology. To continue the example in which x denotes blood pressure, perhaps a large clinical trial has provided an estimate of the change in blood pressure under a new treatment; then the possible effect on y, under the new treatment, should be estimated from the slope in the regression of y on x. Another circumstance is predictive modelling in which future observations are also variable, but not (in the phrase used above) "similarly variable".

Not all of the difference between the treatment and control groups at time 2 (that is, the difference between P2 and S2) can be explained as being an effect of the treatment, because the treatment group and control group did not start out at the same point at time 1. DID therefore calculates the "normal" difference in the outcome variable between the two groups (the difference that would still exist if neither group experienced the treatment), represented by the dotted line Q. (Notice that the slope from P1 to Q is the same as the slope from S1 to S2.) The treatment effect is the difference between the observed outcome and the "normal" outcome (the difference between P2 and Q).

When an assessment is used with the purpose of predicting an outcome (perhaps another test score or some other behavioral measure), a new instrument must show that it is able to increase our knowledge or prediction of the outcome variable beyond what is already known based on existing instruments. A positive example may be a clinician who uses an interview technique as well as a specific questionnaire to determine if a patient has mental illness and has better success at determining mental illness than a clinician who uses the interview technique alone. Thus, the specific questionnaire would be considered incrementally valid. Because the questionnaire in conjunction with the interview produced more accurate determinations, and added information for the clinician, the questionnaire is incrementally valid.

In probability and statistics, a mixture distribution is the probability distribution of a random variable that is derived from a collection of other random variables as follows: first, a random variable is selected by chance from the collection according to given probabilities of selection, and then the value of the selected random variable is realized. The underlying random variables may be random real numbers, or they may be random vectors (each having the same dimension), in which case the mixture distribution is a multivariate distribution. In cases where each of the underlying random variables is continuous, the outcome variable will also be continuous and its probability density function is sometimes referred to as a mixture density. The cumulative distribution function (and the probability density function if it exists) can be expressed as a convex combination (i.e.

Originating from early statistical analysis in the fields of agriculture and medicine, the term "treatment" is now applied, more generally, to other fields of natural and social science, especially psychology, political science, and economics such as, for example, the evaluation of the impact of public policies. The nature of a treatment or outcome is relatively unimportant in the estimation of the ATE—that is to say, calculation of the ATE requires that a treatment be applied to some units and not others, but the nature of that treatment (e.g., a pharmaceutical, an incentive payment, a political advertisement) is irrelevant to the definition and estimation of the ATE. The expression "treatment effect" refers to the causal effect of a given treatment or intervention (for example, the administering of a drug) on an outcome variable of interest (for example, the health of the patient).

The case–control is a type of epidemiological observational study. An observational study is a study in which subjects are not randomized to the exposed or unexposed groups, rather the subjects are observed in order to determine both their exposure and their outcome status and the exposure status is thus not determined by the researcher. Porta's Dictionary of Epidemiology defines the case–control study as: an observational epidemiological study of persons with the disease (or another outcome variable) of interest and a suitable control group of persons without the disease (comparison group, reference group). The potential relationship of a suspected risk factor or an attribute to the disease is examined by comparing the diseased and nondiseased subjects with regard to how frequently the factor or attribute is present (or, if quantitative, the levels of the attribute) in each of the groups (diseased and nondiseased).

In statistics, fixed-effect Poisson models are used for static panel data when the outcome variable is count data. Hausman, Hall, and Griliches pioneered the method in the mid 1980s. Their outcome of interest was the number of patents filed by firms, where they wanted to develop methods to control for the firm fixed effects.Hausman, J. A., B. H. Hall, and Z. Griliches (1984): "Econometric Models for Count Data with an Application to the Patents-R&D; Relationship." Econometrica (46), pp. 909–938 Linear panel data models use the linear additivity of the fixed effects to difference them out and circumvent the incidental parameter problem. Even though Poisson models are inherently nonlinear, the use of the linear index and the exponential link function lead to multiplicative separability, more specifically Cameron, C. A. and P. K. Trivedi (2015) "Count Panel Data," Oxford Handbook of Panel Data, ed. by B. Baltagi, Oxford University Press, pp. 233–256 : E[yit ∨ xi1... xiT, ci ] = m(xit, ci, b0 ) = exp(ci \+ xit b0 ) = ai exp(xit b0 ) = μti (1) This formula looks very similar to the standard Poisson premultiplied by the term ai.