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Sample Assignment 1: Understanding Logistic Regression in Academic Research
Scenario-Based Question:
A public health researcher is analyzing a dataset of patients from a regional hospital to determine the influence of lifestyle factors on the likelihood of developing Type 2 Diabetes. The dependent variable is binary (1 = has diabetes, 0 = does not). Independent variables include age, BMI, physical activity level (categorical: Low, Medium, High), and smoking status.

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Expert Solution:

Step 1: Data Preparation and Model Building
Before fitting the model, the categorical variable ‘physical activity level’ is dummy-coded using Medium as the reference category.

The logistic regression model is specified as:

markdown
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Edit
Logit(P(Diabetes=1)) = β0 + β1*Age + β2*BMI + β3*PhysicalActivity_Low + β4*PhysicalActivity_High + β5*SmokingStatus
Step 2: Interpreting Coefficients
After fitting the model using statistical software (e.g., R or SPSS), we obtain the following result:

Variable Coefficient Std. Error z-value p-value
Intercept -4.523 0.891 -5.07 <0.001
Age 0.041 0.008 5.13 <0.001
BMI 0.115 0.027 4.26 <0.001
PhysicalActivity_Low 0.844 0.329 2.56 0.010
PhysicalActivity_High -0.437 0.318 -1.37 0.172
SmokingStatus (Yes = 1) 0.673 0.281 2.39 0.017

Step 3: Interpretation
Age and BMI are significant predictors. For each additional year in age, the odds of having diabetes increase by approximately 4.2%.

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Scenario-Based Question:
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Expert Solution:

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Step 3: Model Summary
Coefficient Estimate Std. Error z-value p-value
AR(1) 0.692 0.081 8.55 <0.001
MA(1) -0.521 0.095 -5.48 <0.001

The residual diagnostics (Ljung-Box test p > 0.05) confirm the model’s adequacy.

Step 4: Forecasting
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Step 5: Conclusion
The ARIMA model provides a robust framework for forecasting unemployment. With consistent updating and residual monitoring, this model could guide economic policy decisions and public employment strategies effectively.

Why These Approaches Matter in Academic Projects
Master-level statistical assignments increasingly mirror real-world data challenges. Instead of textbook-style questions, students now engage with open-ended, scenario-based tasks requiring:

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