Logistic Regression Research: 99% Concordance, Few Significance

Thread starter mathmathRW
Start date Nov 1, 2014
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In summary, logistic regression is a statistical method used to analyze the relationship between categorical dependent variables and independent variables in research studies. A 99% concordance in logistic regression research indicates a high level of agreement between predicted and actual outcomes. "Few significance" means that only a small number of variables have a significant impact on the dependent variable. Logistic regression is important because it helps researchers understand the relationship between variables and make predictions about binary outcomes. However, it has limitations such as assuming a linear relationship and being unable to work with continuous or multi-category dependent variables. It is important to include all relevant variables in the model to avoid biased results.

Nov 1, 2014

mathmathRW

I am doing some research and running a SAS program using logistic regression. The concordance is 99%, but hardly any variables are significant. Can anyone help me understand what this means?

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Nov 4, 2014

chiro

Science Advisor

Hey mathmathRW.

Can you please post your output data from SAS so we can take a look at it?

Related to Logistic Regression Research: 99% Concordance, Few Significance

What is logistic regression research?

Logistic regression is a statistical method used to model the relationship between a categorical dependent variable and one or more independent variables. It is commonly used in research studies to analyze the impact of various factors on a binary outcome, such as success or failure.

What does it mean to have a 99% concordance in logistic regression research?

A 99% concordance in logistic regression research refers to the high level of agreement between the predicted outcomes and the actual outcomes. This indicates that the model is accurately predicting the binary outcome with a high degree of certainty.

What does "few significance" mean in logistic regression research?

"Few significance" in logistic regression research means that there are only a small number of independent variables that have a statistically significant impact on the dependent variable. This could indicate a strong relationship between these variables and the outcome being studied.

Why is logistic regression research important?

Logistic regression research is important because it allows researchers to understand the relationship between multiple variables and a binary outcome. It can help identify which factors have the strongest impact on the outcome and can be used to make predictions about future outcomes.

What are the limitations of logistic regression research?

Like any statistical method, logistic regression research has its limitations. One limitation is that it assumes a linear relationship between the independent variables and the log odds of the dependent variable. Additionally, logistic regression cannot be used with continuous or multi-category dependent variables. It is also important to ensure that all relevant variables are included in the model to avoid biased or inaccurate results.