Objective: Examine the determinants of HealthScore by modeling its relationship with various predictors, including interaction effects and non-linear terms. Tasks: 1. Data Preprocessing: . Load the statsnew.csv dataset into R. . Check for missing values and handle them appropriately (e.g., imputation, removal). • Convert categorical variables (Gender, Education Level, EmploymentStatus, . Marital Status, Geographical Region, AccessToHealthcare, SmokingStatus) into appropriate numerical formats using dummy variables or factor encoding. Explore and visualize the distributions of continuous variables (Age, Income, HoursWorked, JobSatisfaction, NumberOfChildren, Physical ActivityHours, Alcohol Consumption). 2. Feature Engineering: • Create interaction terms between Income and Education Level, and between HoursWorked and JobSatisfaction. Incorporate non-linear relationships by adding polynomial terms (e.g., quadratic terms for Age and PhysicalActivityHours). 3. Model Building: . • Construct a multiple linear regression model with HealthScore as the dependent variable and all other variables (including interaction and polynomial terms) as independent predictors. Use regularization techniques (e.g., Ridge, Lasso) to handle potential multicollinearity and improve model interpretability. 4. Model Diagnostics: • • Assess the assumptions of linear regression: linearity, independence, homoscedasticity, normality of residuals. Detect and address multicollinearity among predictors using Variance Inflation Factor (VIF). Evaluate model performance using metrics such as R-squared, Adjusted R-squared, AIC, and BIC. 5. Model Selection and Validation: • Perform stepwise model selection based on AIC to identify the most parsimonious model. Validate the final model using cross-validation techniques (e.g., k-fold cross-validation). 6. Interpretation: • Interpret the coefficients of the final model, focusing on significant predictors and interaction effects. . Discuss the practical implications of the findings on factors influencing HealthScore.

Objective: Examine the determinants of HealthScore by modeling its relationship with various predictors, including interaction effects and non-linear terms. Tasks: 1. Data Preprocessing: . Load the statsnew.csv dataset into R. . Check for missing values and handle them appropriately (e.g., imputation, removal). • Convert categorical variables (Gender, Education Level, EmploymentStatus, . Marital Status, Geographical Region, AccessToHealthcare, SmokingStatus) into appropriate numerical formats using dummy variables or factor encoding. Explore and visualize the distributions of continuous variables (Age, Income, HoursWorked, JobSatisfaction, NumberOfChildren, Physical ActivityHours, Alcohol Consumption). 2. Feature Engineering: • Create interaction terms between Income and Education Level, and between HoursWorked and JobSatisfaction. Incorporate non-linear relationships by adding polynomial terms (e.g., quadratic terms for Age and PhysicalActivityHours). 3. Model Building: . • Construct a multiple linear regression model with HealthScore as the dependent variable and all other variables (including interaction and polynomial terms) as independent predictors. Use regularization techniques (e.g., Ridge, Lasso) to handle potential multicollinearity and improve model interpretability. 4. Model Diagnostics: • • Assess the assumptions of linear regression: linearity, independence, homoscedasticity, normality of residuals. Detect and address multicollinearity among predictors using Variance Inflation Factor (VIF). Evaluate model performance using metrics such as R-squared, Adjusted R-squared, AIC, and BIC. 5. Model Selection and Validation: • Perform stepwise model selection based on AIC to identify the most parsimonious model. Validate the final model using cross-validation techniques (e.g., k-fold cross-validation). 6. Interpretation: • Interpret the coefficients of the final model, focusing on significant predictors and interaction effects. . Discuss the practical implications of the findings on factors influencing HealthScore.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 15PPS

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