When i do regression analyze, in Model Summary Table, i found Rsquare is very weak like:0,001 or 0.052, and value of sig. in Anova table is greater than 0.05, how can i fix this?
Unless you have an error in your data, this may just simply be the result of the analysis (i.e., that your predictor(s) is/are only weakly related to, and do not significantly predict, the dependent variable).
I agree with Christian Geiser . The result could simply be reflective of the fact that the variables are not related. Here are some things you could do:
1. Try to increase your sample size.
2. Try including other independent variables in the analysis (multiple regression analysis).
3. Check your data for errors.
That's about it. If despite these efforts the results don't improve, then it's time to conclude that the variables are not related.
This problem emerged when I transformed the data into a normal distribution, but the Rsquare was also weak before data transformation.
Model Summary Model | R | R Square | Adjusted R Square | Std. Error of the Estimate ------- | -------- | -------- | -------- | -------- 1 | .750 | .563 | .557 | 2.546
This Model Summary table shows that the regression model has an R of .750, which means that there is a strong correlation between the predicted and actual values of the dependent variable. The R Square of .563 means that the model explains 56.3% of the variance in the dependent variable. The Adjusted R Square of .557 is a more accurate measure of predictive power, and it is also high. The Std. Error of the Estimate of 2.546 means that the model's error is relatively small.
Your model determines your result. Try adding other variables to see if something else will better explain your dependent variable. Then pick the variable with the strongest (highest value) R-squared to run by itself. You will then get a better result and model.
The difference between the value of R and the value of R-Square indicates the presence of excess variability, probably from outliers in the dependent variable or in one or more of the predictor variables. Use SPSS to obtain a histogram of each variable to visually inspect for outliers, or obtain box plots for the same purpose.
check your data again: out layer, coding, and entry too. after that the insignificance of the ANOVA table is telling you that do not proceed to analyze and interpret the table. you have to stop there and search for other method. even it may ask you to change your data analyzes method
The very low value of R-squared and p-value greater than 0.05 indicate that your model is not significant. Your regressors or predictors do not significantly influence your dependent variable.
How to fix this? Make sure that your independent variables are literature-based. There should be theories, studies or literature supporting these variables to have effect on your dependent variable.
Based on my experience, several things can be done: 1. add another variables (as predictor or control), and or 2. add more samples, and or 3. look back at outliers 4. Try other variable indicators
You can make sure and confirm this by reading articles on related topics
A low R-squared value and non-significant p-values in the ANOVA table suggest that the regression model does not explain much of the variability in the dependent variable, and the overall model is not statistically significant.
to fix it try these options:
Check The Model Assumptions:
- Ensure that the assumptions of linear regression are met. These assumptions include linearity, independence of errors, homoscedasticity (constant variance of errors), and normality of errors.
- If linear regression is not appropriate for your data, consider other types of models, such as polynomial regression, decision trees, or machine learning algorithms.
- Check for multicollinearity among independent variables, as it can affect the stability of the coefficients.
- Check if the relationship between the dependent variable and independent variables is linear. If not, you may need to include non-linear terms or use non-linear regression techniques.
Process Your Features:
- Just add relevant features or transforming existing features to better capture the relationship with the dependent variable and Remove irrelevant or highly correlated variables that may be introducing noise into the model.
- Identify and address outliers or influential observations that might be affecting the model's performance. Consider excluding or transforming them if necessary and check the size of your sample (maybe it this low).
- If there are concerns about outliers affecting the model, consider using robust regression techniques that are less sensitive to extreme values.
- Talk to experts in the field to gather insights into the potential factors influencing the dependent variable and to validate the model's structure.
- After fine-tuning, re-run the model again and see the results
Dear Salwa :
You check in SPSS Outlier Test (looking for abnormal data). For moon signs the data is indeed less normal but can still be tolerated, We can discard data marked with stars or add respondents by distributing additional questionnaire data if the number of respondent data is slightly above the minimum.
Process outlier as below:
Klik Analyze > Descriptive Statistics > Explore > Var X1, X2,X3,Y (one by one Var) > Statistic > OK
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