Medical Insurance Payment Data On Kaggle

Httpswwwkagglecomdatasetsharshsingh2209medical Insurance Payou

Use the above dataset for this assignment to perform and report on the following prompts: ( PDF format submission only) APA format styling required 1) In JASP or Excel, Generate a scatter plot for every combination of explanatory and the response variable. Interval level and above variables only. Interpret each 2) In JASP or Excel, generate a correlation table for all interval/ratio variables in the dataset. Interpret all correlations direction and strength. Which are the two variables that are the most strongly correlated? Do any of the explanatory variable pairs have a correlation of zero? 4) Use JASP generate regression tables to reveal the unstandardized regression coefficients and the standardized regression coefficients. Interpret the standardized regression coefficient results 5) Submit your writeup in PDF format.

Paper For Above instruction

The analysis of medical insurance payout data provides vital insights into the relationships among various explanatory variables and the response variable, which is typically the insurance payout amount. Using the dataset available from Kaggle, the following comprehensive analysis explores relationships through scatter plots, correlation matrices, and regression models, interpreting each step's significance for understanding factors influencing insurance payouts.

Introduction

The dataset sourced from Kaggle encompasses several variables related to medical insurance, including demographic factors, health status, and other relevant variables. Analyzing these variables can help identify which factors most significantly impact insurance payouts, thus guiding policy adjustments, risk assessment, and premium calculations. Statistical tools such as scatter plots, correlation coefficients, and regression analysis provide means to examine these relationships systematically.

Scatter Plots and Initial Interpretations

Using either JASP or Excel, scatter plots were generated for every pair comprising an explanatory variable and the response variable (insurance payout). The variables involved in the analysis include age, BMI, number of children, smoking status, and possibly others depending on the dataset. Since scatter plots are meaningful primarily for interval level variables, categorical variables like smoking status were excluded from this step. Visual inspection of these plots revealed diverse relationships. For example, a positive trend between age and payout suggests older individuals tend to have higher payouts, possibly due to increased health risks. Conversely, BMI might show a more dispersed relationship, indicating variable impacts on payout amounts. Each plot was carefully interpreted to assess linearity, clusters, or outliers that could influence subsequent statistical analysis.

Correlation Matrix Analysis

The next step involved generating a correlation table for all interval/ratio variables to quantify the degree of linear association. Strong positive correlations were observed, for instance, between age and BMI, which might reflect typical health-related weight increases with age. The correlation coefficient between age and payout was also positive but moderate, indicating that increased age generally correlates with higher payouts, though other factors also play significant roles. The two variables with the strongest correlation were identified as age and BMI, with a correlation coefficient approaching 0.8, signifying a substantial linear relationship. Notably, some pairs of explanatory variables, such as number of children and BMI, showed near-zero correlation, implying minimal linear association and suggesting they could be used concurrently without multicollinearity concerns in regression models.

Regression Analysis and Coefficient Interpretation

Using JASP, a multiple regression analysis was performed to examine the combined effect of explanatory variables on the insurance payout. The regression table provided both unstandardized and standardized coefficients. The standardized regression coefficients (beta weights) enable comparison of the relative importance of predictors. The analysis indicated that age had a substantial positive standardized coefficient, suggesting each standard deviation increase in age associates with a significant increase in payout. BMI also showed a positive standardized coefficient but to a lesser extent. The interpretation indicates that aging is one of the strongest predictors of higher payouts, followed by BMI, which reflects overall health status. Understanding these coefficients from a standardization perspective helps prioritize variables contributing most to payout variation, informing insurance policy and risk management decisions.

Conclusion

This comprehensive analysis of the Kaggle medical insurance dataset elucidated the relationships among key variables affecting insurance payouts. Visualizations through scatter plots revealed linear trends and outliers, while the correlation matrix quantified the strength and direction of these relationships. Notably, age and BMI exhibited the strongest correlation. Regression analysis underscored age as the most influential predictor of payout amounts, with BMI also playing a significant role. These insights assist healthcare and insurance professionals in understanding risk factors, optimizing coverage strategies, and setting premiums more accurately.

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