Q: Difference between dummy and categorical variable?

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To capture and express differences between group means produced by categorical predictors (independent variables) using correlational/regression techniques, one typically encodes categorical variables vis-a-vis dummy, contrast or effects coding to produce vectors, each of which define a group difference in the form of a slope coefficient. Each vector can be thought of as a predictor variable which targets a single degree of freedom difference (or the difference between two group means). The correlation between a vector and the criterion (dependent variable), when squared, expresses the difference between two group means as a proportion of variance accounted for (the proportion of variance in DV accounted for by being either in grp1 or grp2). Coding allows one to easily partition the between groups variance. The vectors are always single degree of freedom values (two coded values for two groups). How many vectors? The number is equal to the degrees of freedom of the between groups term or one less than the number of groups. Take a look at Kepple & Zeddeck 1989 "data analysis for research designs". Hope this helps.

A dummy variable is a variable that takes on the values 1 and 0; 1 means something is true. These are used in statistical analyses.

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An independent variable is the variable you have control over, what you can choose and manipulate. It is usually what you think will affect the dependent variable. In some cases, you may not be able to manipulate the independent variable. It may be something that is already there and is fixed, something you would like to evaluate with respect to how it affects something else, the dependent variable like color, kind, time. Example: You are interested in how stress affects heart rate in humans. Your independent variable would be the stress and the dependent variable would be the heart rate. You can directly manipulate stress levels in your human subjects and measure how those stress levels change heart rate.

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.

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To capture and express differences between group means produced by categorical predictors (independent variables) using correlational/regression techniques, one typically encodes categorical variables vis-a-vis dummy, contrast or effects coding to produce vectors, each of which define a group difference in the form of a slope coefficient. Each vector can be thought of as a predictor variable which targets a single degree of freedom difference (or the difference between two group means). The correlation between a vector and the criterion (dependent variable), when squared, expresses the difference between two group means as a proportion of variance accounted for (the proportion of variance in DV accounted for by being either in grp1 or grp2). Coding allows one to easily partition the between groups variance. The vectors are always single degree of freedom values (two coded values for two groups). How many vectors? The number is equal to the degrees of freedom of the between groups term or one less than the number of groups. Take a look at Kepple & Zeddeck 1989 "data analysis for research designs". Hope this helps.

To interpret the coefficient of a dummy variable is to follow all of the steps of the equation that is being used as if the dummy variable was a real one.

It is a variable which usually the values 0 and 1 depending on the presence and absence of a particular factor in a set of trails. Similarly, the gender of a person could be coded as 0 (= Male) and 1 (= Female) - or the other way around. The actual coding does not matter but it allows for comparisons between the two sub-sets of the population.

It can be.

quasi experiment simply exists

Add a negative dummy variable to the side that was superior, or a positive dummy variable to the side that was inferior. For example, X > 5 could be replaced by X-a = 5 or X = 5+b where a and b are positive.

In C, the sizeof operator can be considered a dummy operator because it does not perform any operations on the data but simply returns the size in bytes of a variable or a data type.

A dummy variable is a variable that takes on the values 1 and 0; 1 means something is true. These are used in statistical analyses.

i dont know u tell me you dummy

Maybe you should spend less time stealing and wondering this and, instead, learn the difference between "your" and "you're," dummy.

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.

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