Examples of using The dependent variable in English and their translations into Ukrainian
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The dependent variable, Y{\displaystyle Y}.
In many cases, however, the magnitude of effects found in the dependent variable may not just depend on.
The dependent variable is usually the objective of the research.
In order toperform a regression analysis the user must provide information about the dependent variable Y{\displaystyle Y}.
Here the dependent variable(GDP growth) is presumed to be in a linear relationship with the changes in the unemployment rate.
In this situation, the dependent variable is the amount of money you make because it isdependent on how much time you work.
Predictors that had a statistical relationship with the dependent variable with the level of significance(p= 0, 1) and fewer were still included in the regression model.
A VAR with p lags can always be equivalently rewritten as aVAR with only one lag by appropriately redefining the dependent variable.
Vice versa, changes in the dependent variable may only be affected due to a demoralized control group, working less hard or motivated, not due to the independent variable. .
That will, for example,minimize the distance between the measured and predicted values of the dependent variable Y{\displaystyle Y}.
If a log transformation is applied to the dependent variable only, this is equivalent to assuming that it grows(or decays) exponentially as a function of the independent variables. .
The dependent variables for the analysis of PK-PD included the change in PFT values for FEV1, FEV1percent predicted FEF25-75%and high-flow at 7 and 14 days from baseline(before administration of the dose on day 1) and the change log10SOME in each of these days from baseline.
This third requirement distinguishes discretechoice analysis from forms of regression analysis in which the dependent variable can(theoretically) take an infinite number of values.
If an independent variable is found to have an effect in only one of 20 studies, the meta-analysis will tell you that that one study was an exception and that, on average,the independent variable is not influencing the dependent variable.
Regression analysis is performed to determine the equation that combines the dependent variable Y with the independent variables Xg If, it is possible to predict how its value varies according to the changes Xg Not always, but most often linear regression is defined as a straight line:.
How to fix: If the dependent variable is strictly positive and if the residual-versus-predicted plot shows that the size ofthe errors is proportional to the size of the predictions(i.e., if the errors seem consistent in percentage rather than absolute terms), a log transformation applied to the dependent variable may be appropriate.
If an independent variable is having an effect in most of the studies, the meta analysis is likely to tell us that, on average,it does influence the dependent variable.
In scientific experimental settings, researchers often manipulate a variable(the independent variable) to see what effect it has on a second variable( the dependent variable).[3] For example, a researcher might, for different experimental groups, manipulate the dosage of a particular drug between groups to see what effect it has on health. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences.
Functions need an independent variable in order to produce a determinate result,but in this case the independent variable of one function is the dependent variable of the other.
Finding a solution for unknown parameters β that will, for example,minimize the distance between the measured and predicted values of the dependent variable Y(also known as method of least squares).
There are two major types of grouping: data binning of a single-dimensional variable, replacing individual numbers by counts in bins; and grouping multi-dimensional variables by some of the dimensions(especially by independent variables), obtaining the distribution of ungrouped dimensions(especially the dependent variables).
Quantities such as regression coefficients are statistical parameters in the above sense, because they index the family ofconditional probability distributions that describe how the dependent variables are related to the independent variables. .
In scientific experimental settings, researchers often change the state of one variable(the independent variable) to see what effect it has on a second variable( the dependent variable).
In the last case, the regression analysis provides the tools for: Finding a solution for unknown parameters β{\displaystyle\beta} that will, for example,minimize the distance between the measured and predicted values of the dependent variable Y{\displaystyle Y}(also known as method of least squares).
Under certain statistical assumptions, the regression analysis uses the surplus of information to provide statistical information about the unknown parameters β{\displaystyle\beta}and predicted values of the dependent variable Y{\displaystyle Y}.
In statistics, simple linear regression is a linear regression model with a single explanatory variable.[ 1][ 2][ 3][ 4] That is, it concerns two-dimensional sample points with one independent variable and one dependent variable(conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function(a non-vertical straight line) that, as accurately as possible,predicts the dependent variable values as a function of the independent variables. .