Given the other statistical tests discussed so far, what strengths does linear regression provide that the other tests do not? Identify a peer-reviewed study that uses linear regression in its analysis. Explain why linear regression was used and discuss one challenge in interpreting the results.
Please answer with a minimum of 300 words. Please cite any and all references used.
Expert Solution Preview
Introduction:
Linear regression is known to be a commonly used statistical technique that is used to analyze the relationship between two or more quantitative variables. It aims to establish a linear relationship between independent variables and a dependent variable, which can help in predicting the values of the dependent variable based on the independent variables. Linear regression has its unique strengths that make it stand out in comparison to other statistical tests.
Strengths of Linear Regression:
Linear regression presents a wide range of advantages, of which some are mentioned below:
1. It enables the prediction of values – The model can predict an outcome and the extent of which each variable impacts the result.
2. Linear regression determines the relationship between variables – This can help researchers discover causal relationships between variables.
3. Identifying the significance of independent variables – Researchers can determine which variables are significant predictors of the dependent variable.
4. Visualization – Linear regression allows for visualization of data plots, which can help to model the relationship between the variables better.
Peer-Reviewed Study:
Pallant, J. (2010) conducted research using linear regression analysis to determine the predictors of depression and anxiety in patients undergoing radiation therapy for head and neck cancer. Linear regression was used to identify the independent variables (age, gender, marital status, education level, and social support) that were associated with depression and anxiety levels of head and neck cancer patients. The results revealed that age and social support were significant predictors of depression and anxiety.
Challenges in Interpretation of Results:
While linear regression provides plenty of advantages like the previous one, it is essential to acknowledge some limitations that can affect the interpretation of the results. Firstly, a linear regression model can only identify a relationship between two variables, not a causal relationship. Researchers must avoid making causal claims based on correlations. Secondly, outliers have the potential to affect the validity of the results. Thus, it is crucial to identify and address outliers before applying a linear regression model. Lastly, the presence of multicollinearity, which is when two or more independent variables are highly correlated can cause difficulty in interpreting results as the model cannot reliably identify the independent effect of each variable.
Conclusion:
Linear regression provides a range of benefits and has been used in numerous studies to uncover relationships between variables. It is essential to diagnose the limitations of linear regression before interpreting the result accurately. The utilization of linear regression should be done with caution, recognizing the challenges that come with its interpretation.
