Effect of changing values of Dummy variables.

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In summary, the conversation discusses the results of a regression with a single dummy variable and the effects of changing the dummy variable values to .2 + 3(x). The coefficient of the dummy term decreased by a factor of three, but the impact of the added 2 is unclear. The suggestion is to look at the intercept term in the results/output for the effects of the 2. It is noted that the intercept is different in the second regression, but the exact relationship is still unclear due to the smaller coefficient on the new dummy term.
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smith007
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I have run a regression using a single dummy variable and have my outputs. I then changed the values of my dummy variables from 1 and 0 to . 2 + 3(x) where x was 1 or 0 with the same 1 and 0 positions in my data as the original regression. I can see that the coeffeicient of the dummy terms have decreased by an exact factor of 3 but I cannot figure out how the 2 changes the output. Any suggestions where I can look in my results/output for the effects of the 2? Thanks.
 
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  • #2
The intercept term.
 
  • #3
Thank you for the reply. I had noticed that the intercept term was different on the second regression but I couldn't seem to figure out the exact relationship. It does not change the intercept by exactly two because of the smaller coefficient on my new dummy term.

Thanks again.
 

Related to Effect of changing values of Dummy variables.

1. What are dummy variables?

Dummy variables are categorical variables that represent different categories or levels of a particular feature. They are used in statistical models to represent the presence or absence of a particular characteristic.

2. How do dummy variables affect statistical models?

Dummy variables can affect statistical models by providing a way to include categorical data in a regression model. They can help to control for different levels of a categorical variable and determine the effect of each level on the outcome variable.

3. What is the purpose of changing the values of dummy variables?

Changing the values of dummy variables can help to improve the interpretation of the regression model. By changing the reference category of a dummy variable, the effect of each level can be compared to a different reference point, providing a more comprehensive understanding of the relationship between variables.

4. How do we determine the best values for dummy variables?

The best values for dummy variables depend on the specific research question and the data being analyzed. It is important to carefully consider the reference category and the number of dummy variables included in the model to ensure accurate and meaningful results.

5. Can dummy variables cause multicollinearity in a model?

Yes, dummy variables can cause multicollinearity in a model if they are highly correlated with each other. This can happen if there are too many dummy variables included in the model, or if the categories are not distinct enough. It is important to check for multicollinearity when using dummy variables in a regression model to ensure the accuracy of the results.

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