There are various forms. In linear programming, a dummy variable may be used to convert an inequality into an equation. For example x < 10 can be written as x + u = 10 where u > 0. In this case, it is also called a slack variable. Dummy variables are used in regression to indicate the presence or absence of a factor, or for binary variables. For example, male/female could be coded numerically as 0/1 where, because the question is binary, the exact coding does not matter.
There is not enough information to say much. To start with, the correlation may not be significant. Furthermore, a linear relationship may not be an appropriate model. If you assume that a linear model is appropriate and if you assume that there is evidence to indicate that the correlation is significant (by this time you might as well assume anything you want!) then you could say that the dependent variable decreases by 0.13 units for every unit change in the independent variable - within the range of the independent variable.
A correlation coefficient can only range from -1.0 to 1.0 so a 50 is not possible. Did you mean .5?
The further the correlation coefficient is from 0 (ie the closer to ±1) the stronger the correlation.Therefore -0.75 is a stronger correlation than 0.25The strength of the correlation is dependant on the absolute value of the correlation coefficient; the sign of the correlation coefficient gives the "relative" slope of correlation line:+ve (0 to +1) means that as one variable increases the other also increases;-ve (0 to -1) means that as one variable increases the other decreases.
No, it depends upon the size of the coefficient of correlation: the closer to ±1 the stronger the correlation.When the correlation coefficient is positive, one variable increases as the other increases; when negative one increases as the other decreases.
Depends on the instrumented variable. For example if the instrumented variable x is a dummy (z an instrument) the way you should interpret the IV coefficient is, depending on the treatment of the endogenous variable y (logs, levels), in average, ceateris paribus, the effect over y of x is (b% or 100b%) for those observations for which the z is present.
This is my best shot. I've been trying to find this answer since I'm doing regressions right now. Let's say you have a dummy variable "male" where 1 = male, 2 = female. You regress: toads_owned = c(1) + c(2)*male You get the result: MALE: Coefficient: 2 T-test: 3.1 toads_owned = c(1) + 2*male So now, I think that means that if you are a male, you are likely to own 2 more toads on average than if you were a female. The coefficient on a dummy variable simply says how different you are from the base group (the group that equals 0) if you equal 1.
The coefficient is in front of a variable.
Yes, a coefficient of a variable can be negative.
The coefficient is 7 and the variable is x
It called the coefficient of a variable. As an example 16x. 16 would be the coefficient and x would be the variable or term.
the coefficient of the variable
The numerical factor in a term with a variable is the coefficient. It is the number that multiplies the variable. For example, in the term 3x, the coefficient is 3.
A coefficient is the number in front of a variable. For example, consider the expression '2y' y is the variable 2 is the coefficient
A variable is a part of a term which can change. A coefficient is a numerical constant, associated with a variable. For example, in the term 3x^2 , 3 is the coefficient, while x is a variable.
The numerical value that comes before the variable or, if none, the coefficient is 1.The numerical value that comes before the variable or, if none, the coefficient is 1.The numerical value that comes before the variable or, if none, the coefficient is 1.The numerical value that comes before the variable or, if none, the coefficient is 1.
the coefficient of the variable