Logistic Regression Research: 97% Concordance, No Sig Variables

In summary, the speaker has conducted an independent research project and has created a logistic regression program in SAS. However, despite a percent concordance of 97%, there are few significant variables. They are seeking help in understanding the reasons for this, which could potentially include dependence issues and suggestions to reduce the model and use stepwise regression techniques to identify the most significant factors.
  • #1
mathmathRW
8
0
I am doing an independent research project and I have written a logistic regression program in SAS. The percent concordance is 97%, but hardly any variables are significant. Can anyone help me understand why this would happen?
 
Physics news on Phys.org
  • #2
There are many possible reasons for this. Would you be able to describe the situation in more detail?
If all of the significance is in only a few variables, then you will not have many significant variables.
Otherwise, you may have some dependence issues, and the program can't determine which variables are better than others.
I would try reducing the model, and then applying some stepwise regression techniques to find the most significant factors.
 

1. What is the purpose of logistic regression research?

Logistic regression research is used to analyze the relationship between one or more independent variables and a binary outcome variable. It is commonly used to predict the probability of an event occurring based on certain factors.

2. What does the 97% concordance rate mean in logistic regression research?

The 97% concordance rate indicates that the logistic regression model has a high level of accuracy in predicting the outcome variable. This means that the model correctly predicts the outcome 97% of the time.

3. What does it mean if there are no significant variables in logistic regression research?

If there are no significant variables in logistic regression research, it means that none of the independent variables have a statistically significant impact on the outcome variable. This could indicate that the model is not a good fit for the data or that there are other variables that need to be included in the analysis.

4. How is logistic regression different from linear regression?

Logistic regression is used for predicting binary outcomes, while linear regression is used for predicting continuous outcomes. Logistic regression also uses a different type of regression equation and a different method for estimating the parameters.

5. What are some limitations of logistic regression research?

One limitation of logistic regression research is that it assumes a linear relationship between the independent variables and the logit of the outcome variable. It also requires a large sample size to produce accurate results. Additionally, logistic regression is susceptible to overfitting if too many variables are included in the model.

Similar threads

I Exploring OLS and GLM Models: Understanding the Link Function and Coefficients
    • Set Theory, Logic, Probability, Statistics
Replies
3
Views
839
I Coefficient sign flip in linear regression with correlated predictors
    • Set Theory, Logic, Probability, Statistics
Replies
13
Views
2K
Logistic Regression Research: 99% Concordance, Few Significance
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
MHB Diff-in-Diff Regression: 6 Var, Interaction Terms & Estimate
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
6K
I What are the commonly used estimators in regression models?
    • Set Theory, Logic, Probability, Statistics
Replies
23
Views
2K
A Continuous output: logistic vs linear regression
    • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K
B Comparing Approaches: Linear Regression of Y on X vs X on Y
    • Set Theory, Logic, Probability, Statistics
2
Replies
64
Views
3K
I Lack of Fit in Ordinal Regression -- Analysis/Alternatives?
    • Set Theory, Logic, Probability, Statistics
Replies
21
Views
2K
I Can I get thresholds from logistic regression coefficients?
    • Set Theory, Logic, Probability, Statistics
Replies
12
Views
1K
A R2 Addition in OLS Regression: Unrelated Variables?
    • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K

Hot Threads

Recent Insights

Back
Top