Admissions.csv simulates administrative data where each row represents a unique admission to a hospital. Lab.csv simulates results for patients who had laboratory testing (e.g. blood counts) in their admission. Transfusions.csv simulates information on patients who underwent a blood transfusion in their admission. 1. Impute the missing charlson_comorbidity_index values in any way you see fit, with the intention that this variable will be used as a predictor in a statistical model. 2. Determine if there is a significant difference in sex between patients who had an rbc_transfusion and patients that did not. Fit a linear regression model using the result_value of the “Platelet Count” lab tests as the dependent variable and age, sex, and hospital as the independent variables. Briefly interpret the results. 4. Create one or multiple plots that demonstrate the relationships between length_of_stay (discharge date and time minus admission date and time), charlson_comorbidity_index, and age. 5. You are interested in evaluating the effect of platelet transfusions on a disease. The patients with platelet_transfusion represent the selected treatment group. Select a control group in any way you see fit. How could you improve your selection if you had more data and access to any clinical variable you can think of? 6. Fit a first-iteration statistical model of your choosing to predict the result_value of the “Hemoglobin” lab tests and evaluate its performance. How could you improve the model if you had more data and access to any clinical variable you can think of?

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