Examples of using The dependent variable in English and their translations into Vietnamese
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Colloquial
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Ecclesiastic
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Computer
When the independent variable changes in your experiment, so thus the dependent variable.
Ordered logistic regression is used when the dependent variable is ordered, but not continuous.
A The dependent variable is the outcome that the researcher wishes to predict or explain.
If p= more than 0.05, the independent variable does not impact the dependent variable.
It is used when the dependent variable's error terms are correlated with the independent variables.
Within the examples above, the dependent variable will be the measured impact of caffeine or fertilizer.
In the examples above, the dependent variable would be the measured impact of caffeine or fertilizer.
But in(22.5.2) the dependent variable is change in the GDP over the previous quarter.
(Also, a decrease in the independent variable will mean a decrease in the dependent variable.).
Independent variable is changed then the dependent variable is measured to come up with the accurate conclusion.
for instance, r is the independent variable and A is the dependent variable.
A coefficient of correlation of +0.8 or -0.8 indicates a strong correlation between the independent variable and the dependent variable.
In reality there are likely to be many independent variables that cause a change in the amount of the dependent variable.
(It is likely that there will be many independent variables that cause the change in the amount of the dependent variable.).
is a positive amount, such as +0.80, it means the dependent variable is increasing when the independent variable is increasing.
Hence, the monthly electricity cost(the dependent variable) will increase when there is an increase in the number of production machine hours(the independent variable). .
Expanding the example above, a 2-way ANOVA can examine differences in IQ scores(the dependent variable) by Country(independent variable 1) and Gender(independent variable 2).
of correlation is a positive amount, such as +0.80, it means an increase in the independent variable will result in an increase in the dependent variable.
-0.20 indicates that only 4%(0.20 x 0.20) of the change in the dependent variable is explained by the change in the independent variable. .