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  Encyclopedia of Keywords > Independent Variable > Dependent Variable   Michael Charnine

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    This Review contains major "Dependent Variable"- related terms, short phrases and links grouped together in the form of Encyclopedia article.


  1. A dependent variable is a variable whose values are presumed to change as a result of the independent variable.
  2. A dependent variable is a variable that the investigator measures to determine the effect of the independent variable.
  3. A dependent variable is one which changes as a result of the independent variable being changed, and is put on the Y-axis in graphs. (Web site)
  4. A dependent variable is what you measure in the experiment and what is affected during the experiment.
  5. Dependent variable is the number of passersby staring at the sky out of ten passer-bys.


  1. Suppose you have p dependent variables, k parameters for each dependent variable, and n observations. (Web site)
  2. That is, the independent variables are observed for such observations but the dependent variable is not.


  1. The variable of interest, y, is conventionally called the " dependent variable ".
  2. MA models include past observations of the innovations noise process in the forecast of future observations of the dependent variable of interest.
  3. Macro variable OUTCOME is the main outcome of interest and should be a binary variable (also known as the dependent variable). (Web site)


  1. The residual,, is the difference between the value of the dependent variable predicted by the model, and the true value of the dependent variable y i.
  2. The error, also called the residual, is the difference between the expected and predicted value of the dependent variable.
  3. A residual is defined as the difference between the values of the dependent variable and the model. (Web site)


  1. In practice, it is not important that you use the exact estimated value of lambda for transforming the dependent variable. (Web site)

Single Dependent Variable

  1. When conducting an anova we have a single dependent variable and a number of explanatory factors. (Web site)
  2. Rather, it applies to any case when two or more independent variables are used simultaneously to explain variations in a single dependent variable.
  3. Either a single dependent variable or multiple (within subject) dependent variables are generated according to the specified model.


  1. By analyzing how a single dependent variable (y) is affected by the values of one or more independent variables (x), you can predict what y will be given x. (Web site)


  1. The survey yielded the dependent variable (health status change), the key independent variables (patient perceptions of care), and covariates. (Web site)


  1. This variable captures all other factors which influence the dependent variable y i other than the regressors x i. (Web site)
  2. Next, one needs to determine exactly which of the factors significantly affected the dependent variable of interest. (Web site)
  3. With the SEM approach, the very definition of the factors is achieved so as to maximize the amount of variance explained in the dependent variable.


  1. Dependent variable In an experimental setting, any variable whose values are the results of changes in one or more independent variables.
  2. In both of these types of studies, the effect of changes of an independent variable on the behavior of the dependent variable are observed.
  3. In other words, the dependent variable is the thing that changes as a result of you changing something else. (Web site)


  1. The dependent variable is the variable that is being observed, which changes in response to the independent variable. (Web site)
  2. This effect is also called the “response” or the “relation”. The dependent variable is the response to the independent variable. (Web site)
  3. In other words, a dependent variable is the result observed in response to the independent variable.


  1. Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. (Web site)
  2. For example, linear regression applies when the researcher compares 1 continuous dependent variable and 1 continuous independent variable. (Web site)
  3. Multiple regression applies when the researcher compares 2 or more continuous independent variables against 1 continuous dependent variable. (Web site)


  1. Characteristics of the Dependent Variable: It is assumed that y is interval, continuous, and normally distributed.
  2. Both the independent and dependent variable(s) can be categorical, ordinal, interval or ratio scaled.


  1. In my very, very limited understanding of statistics, multiple regression can only have one dependent variable (political participation in my case).
  2. The multinomial logit model also assumes that the dependent variable cannot be perfectly predicted from the independent variables for any case.
  3. In that case, the dependent variable can only take on 3 distinct values, and the distribution of the dependent variable is said to be multinomial. (Web site)


  1. Planned comparison and post-hoc tests test the significance of differences in levels of an independent factor with respect to a dependent variable.
  2. The first function maximizes the differences between the values of the dependent variable.
  3. ANCOVA provides a mechanism for assessing the differences in dependent variable scores after statistically controlling for the covariate. (Web site)


  1. However, with large samples, groups may be found to differ significantly on a dependent variable, but these differences in effect size may be small. (Web site)
  2. They go into the regression analysis as dummy variables and measures the difference in the mean value of the dependent variable between groups.
  3. We give one group the treatment, and then we measure the groups on the dependent variable.


  1. As the independent variable explains less and less of the variation in the dependent variable, the value of r2 falls toward zero.
  2. Often, the dependent variable is dichotomous, taking the value of zero or one. (Web site)
  3. Put another way, this F statistic tests whether the R square proportion of variance in the dependent variable accounted for by the predictors is zero. (Web site)


  1. This inclusion reflects a model where the dependent variable is not the same Poisson(mu) but it is a sum of N Poisson(mu).
  2. This means that the dependent variable equals the sum of a series of two-dimensional partial regressions. (Web site)
  3. Sum of squared errors(SSE) measures the unexplained variation in the dependent variable.


  1. It works by measuring the fraction of total variation in the dependent variable that can be explained by variation in the independent variable.

Regression Model

  1. Compute a regression model with PRICE as the dependent variable and CUST as a fixed factor (not a covariate).
  2. Also, the number of periods that an independent variable in a regression model is "held back" in order to predict the dependent variable. (Web site)
  3. The dependent variable would have only two values (high performer and low performer) and would thus violate important assumptions of the regression model. (Web site)


  1. It shows the proportion of the total variance of the dependent variable explained by the regression model.
  2. When the dependent variable in a regression model is a proportion or a percentage, it can be tricky to decide on the appropriate way to model it. (Web site)


  1. However, the regression coefficients can be useful when you want to make predictions for the dependent variable based on the original metric of the factors. (Web site)
  2. The multiple regression model can be used to make predictions about the dependent variable. (Web site)


  1. It has no prediction ability because for every value of the independent variable, the prediction for the dependent variable would be the same. (Web site)
  2. In this empirical relation, the regression coefficients represent the independent contributions of each variable to the prediction of the dependent variable.
  3. In the parlance of linear filter theory, the data set whose prediction is desired (the dependent variable) is termed the primary data channel. (Web site)


  1. Regression sum of squares (RSS) measures the variation in the dependent variable explained by the independent variable.
  2. Advanced topics include generalized least squares, instrumental variables, nonlinear regression, and limited dependent variable models. (Web site)


  1. In analysis, researchers usually want to explain why the dependent variable has a given value. (Web site)
  2. However, the dependent variable in this analysis is continuous. (Web site)
  3. Thus, the dependent variable for the analysis is the variable oxygen. (Web site)

Linear Regression

  1. When a linear regression is heteroskedastic its error terms vary and the model may not be useful in predicting values of the dependent variable.
  2. In linear regression a single independent variable, y, is regressed against a single dependent variable, x. (Web site)
  3. Linear regression refers to a linear estimation of the relationship between a dependent variable and one or more independent variables. (Web site)


  1. In this case the equation is named for the dependent variable, LHUR. (Web site)


  1. The dependent variable should have the same variance in each category of the independent variable. (Web site)
  2. In other words, you would guess the modal value of the dependent variable within each category of the independent variable. (Web site)
  3. The dependent variable is always category (nominal scale) variable while the independent variables can be any measurement scale (i.e. (Web site)


  1. Specifically, it is the proportion of the total variability (variance) in the dependent variable that can be explained by the independent variables. (Web site)
  2. Both examine a dependent variable and determine the variability of this variable in response to various factors. (Web site)
  3. Multiple Linear Regression: More than one independent variable is used to account for the variability in one dependent variable.


  1. The variable calculated for the sum of Fugl-Meyer (sumFM) outcome in each session was used as the dependent variable.
  2. The dependent variable is exclusive breast feeding at discharge, and birth weight in grams has a highly significant effect on the outcome (p=0.005).

Continuous Variable

  1. The dependent variable must be a continuous variable and will always appear on the left hand side of the equation. (Web site)

Statistical Test

  1. The number of independent and dependent variable in the experiment also affect which statistical test to choose. (Web site)
  2. A statistical test assesses the relationship between a dependent variable and one or more independent variables. (Web site)

Statistical Tests

  1. Statistical tests usually have a dependent variable and one or more independent variables. (Web site)


  1. Indeed, the general linear model can be seen as an extension of linear multiple regression for a single dependent variable. (Web site)
  2. The relationship between the dependent variable Y and the independent variable X is linear in the slope and intercept parameters a and b.


  1. Linearity implies that each independent variable has a linear (or straight-line) relationship with the dependent variable. (Web site)

Normal Distribution

  1. A generalized linear model using a normal distribution requires the dependent variable to be on an interval or stronger scale.
  2. Normal Distribution: - The dependent variable should be normally distributed within groups. (Web site)

Good Model

  1. In a good model, the mean of the dependent variable will be greater than 1.96 times SEE.
  2. In a good model, SEE will be markedly less than the standard deviation of the dependent variable.


  1. In measuring the acceleration of a vehicle, time is usually the independent variable and speed is the dependent variable. (Web site)

Perceived Value

  1. Each latent variable, in turn, has an arrow pointing toward the dependent variable, in this case the perceived value (utility) of Acceleration.


  1. Regression analysi s is a technique which investigates and models the relationship between a dependent variable (Y) to independent predictors (Xs). (Web site)
  2. This section deals with models in which the dependent variable is discrete.

Structural Model

  1. An exogenous variable is a variable that never appears as a dependent variable in any equation in a structural model. (Web site)
  2. An endogenous variable is a variable that appears as a dependent variable in at least one equation in a structural model. (Web site)

Pretty Straightforward

  1. The dependent variable I hope is pretty straightforward.  Put in your continuous dependent variable. (Web site)
  2. The dependent variable I hope is pretty straightforward.

Explanatory Variable

  1. A relationship is causal if a change in one variable (the explanatory variable) produces a change in the other variable (the dependent variable).
  2. Multiple regression The estimated relationship between a dependent variable and more than one explanatory variable. (Web site)
  3. A linear regression line has an equation of the form Y = bX+ a, where X is the explanatory variable and Y is the dependent variable. (Web site)

Indirect Effect

  1. A shift in the explanatory variable has an indirect effect on the dependent variable for other members of the panel but no direct effect. (Web site)
  2. When an exogenous variable has an effect on the dependent variable, through the other exogenous variable, then it is said to be an indirect effect. (Web site)


  1. Statistical tests of the effect of the manipulated variable on the dependent variable (not the measured manipulation check) affirm this. (Web site)
  2. Manipulation checks do NOT test whether a manipulation actually drives variation in the dependent variable. (Web site)


  1. Manipulations are NOT intended to verify that the manipulated factor caused variation in the dependent variable. (Web site)


  1. Suppose we think of the binary dependent variable y in terms of an underlying continuous probability p, ranging from 0 to 1. (Web site)
  2. I have a binary dependent variable (whether a snake is pregnant or not pregnant). (Web site)
  3. Logistic regression is different from regular regression because the dependent variable can be binary. (Web site)


  1. Independent Variable
  2. Science > Mathematics > Statistics > Regression Analysis
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Related Keywords

    * Ancova * Average Value * Basic Goal * Cases * Change * Coefficient * Coefficients * Conditional Variance * Correlation * Data * Dependent * Dependent Variable Values * Different Effect * Discriminant Analysis * Effect * Estimate * Estimated Value * Estimation * Experiment * Experimenter * Function * Graph * Group Means * Group Membership * High Correlation * Homogeneity * Independent * Independents * Independent Variable * Independent Variables * Interaction Effect * Linear Regression Analysis * Linear Regression Model * Link Function * Logit * Mean * Measure * Model * Null Hypothesis * Poisson Regression * Predictor * Predictors * Predictor Variables * Presumed * Probit * Probit Models * Regression * Regression Analysis * Regression Equation * Relationship * Researcher * Simple Linear Regression * Sst * Third Variable * Unit Change * Value * Values * Variable * Variables * Variance * Variation * X-Axis
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  Originally created: August 12, 2008.
  Links checked: July 22, 2013.
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