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well dependent is when you are are NOT independent so in your daily life you could just have S.E.X and then that is how you become non independent
dependent variables, independent variable, nominal, ordinal, interval, ratio variableThere are three main kinds:Nominal: such as colour of eyes, or gender, or species of animal. With nominal variables there is no intrinsic sense in which one category can be said to be "more" than another.Ordinal: Such as Small/Medium/Large, orStrongly Disagree/Disagree/Indifferent/Agree/Srongly Agree. The categories can be ordered but the differences between pairs is not comparable. For example, it is not really possible to say that the difference betwen Strongly disagree and disagree is the same as (or double or half or whatever) the difference between indifferent and agree.Interval: These are variables where the distance between one pair of values (their interval) can be related to the distance between another pair. Such variables can be subdivided into discrete and continuous.Another way of classifying variables is independent and dependent.The dependent variable is a random variable but the independent variable can be random or non-random.
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
There cannot be one since the answer depends on the form in which the effect is measured: whether the effect is qualitative or quantitative. There are various non-parametric measures of correlation or concordance. For data that are more quantitative there are more powerful tests such as the F-test for independent Normal distributions.
It is a relationship which is non-linear. The same amount of change in the independent variable brings about different amounts of changes in the dependent variable and these differences depend on the initial values of the independent variable.