Analysis of a Real-World Dataset Using Multiple Regression Techniques, UTM

Analysis Steps:

1. Dataset Selection:
   • Select a dataset from a reliable online source.
   • Ensure that the dataset contains at least 300 observations.
   • Include one dependent variable and multiple independent variables.
   • Provide the dataset link in your report.
2. Multiple Regression Model Construction:
   • Use statistical software such as Python, R, MATLAB, or SPSS to construct the regression model.
   • Evaluate the significance of each independent variable based on p-values.
   • Assess the goodness of fit using R-squared and adjusted R-squared values.
   • Test for multicollinearity among the independent variables using the Variance Inflation Factor (VIF).
3. Interpretation of Regression Coefficients:
   • Explain the relationships between the dependent and independent variables.
   • Interpret the regression coefficients in the context of the data.
4. Advanced Analysis Techniques:
   • For Time-Series Data:
     • Check for autocorrelation in the residuals using the Durbin-Watson statistic.
   • For Experimental Data:
     • Split the dataset into training and testing sets.
     • Evaluate the model’s performance using metrics such as Mean Squared Error (MSE).
5. Report Preparation:
   • Summarize your findings in a detailed report.
   • Include:
     • The regression model
     • Interpretation of results
     • Statistical metrics
     • Advanced analysis outcomes
   • Discuss implications, limitations, and recommendations based on the findings.
6. Referencing:
   • Ensure proper referencing for all datasets, tools, and resources used.
   • Cite the title, publisher, year of publication, and resource type.

Evaluation Criteria:

1. Construction and Analysis of the Multiple Regression Model: 10 marks
2. Advanced Analysis: 8 marks
3. Interpretation and Quality of the Report: 7 marks