In this blog entry, we delve into the results of a multiple regression analysis examining the associations between various explanatory variables and the number of nicotine dependence symptoms. Our primary focus is on understanding the relationship between major depression and nicotine dependence symptoms, while considering potential confounding factors.
After adjusting for potential confounding factors, major depression was found to be significantly and positively associated with the number of nicotine dependence symptoms (Beta=1.34, p=0.0001). This suggests that individuals with major depression are more likely to experience a higher number of nicotine dependence symptoms. Additionally, age was found to be significantly associated with nicotine dependence symptoms, with older participants reporting a greater number of symptoms (Beta= 0.76, p=0.025).
The results strongly support the hypothesis that major depression is positively associated with an increased number of nicotine dependence symptoms. The robust statistical evidence, indicated by the low p-value (p=0.0001), reinforces the credibility of this association.
To identify potential confounding factors, additional explanatory variables were systematically added to the model. The results highlighted major depression and age as the main contributors, with no significant confounding variables identified.
These diagnostic plots collectively suggest that the multiple regression model is appropriate for the data. The residuals are normally distributed, indicating good model fit. The standardized residuals plot shows that the model adequately captures the variability in the data, and the leverage plot confirms the absence of influential outliers.
In conclusion, our multiple regression analysis provides valuable insights into the relationship between major depression, age, and nicotine dependence symptoms. The results strongly support the hypothesis, and the diagnostic plots affirm the reliability of the model. Understanding these associations is crucial for developing targeted interventions and improving our understanding of the complex interplay between mental health and addictive behaviors. ```