The premise of the project is closely related to the aspect of the course material but may
explore an avenue, specifically in Excel, that was not discussed in class. You are free to use
internet resources to strengthen your Excel skill and apply it to tackle your project.
• A typed report addressing the questions of the project. No handwritten report is
• Excel file displaying your work. Use multiple sheets of one Excel _le for multiple
tasks rather than using multiple Excel _les.
Data is available for this project on the course website in Canvas with the title Sepsis Data
MIMIC Phase 2. This is a real-world healthcare data extracted from Medical Information
Mart for Intensive Care III (MIMIC III). MIMIC is a relational database containing tables of
data relating to patients who stayed within the intensive care units at Beth Israel Deaconess
The data set for this project includes patients who had the diagnosis of sepsis. Your instructor
had already cleaned the data and prepared it for the statistical analysis. The data includes
1,072 observations. This dataset has less data points than the Phase 1 of the project (1132).
The definition of each column in the data is explained as follows:
SUBJECT ID Patient Identification number
HADM ID Admission number of the patient. (Remember a patient can have multiple hospital admissions)
ADMITTIME Admission time. Because of the privacy reasons, the admission and discharge time has been shifted to a specific value.
DISCHTIME Discharge time
ADMISSION TYPE Types of hospital admission (URGENT or EMERGENCY)
DISCHARGE LOCATION Provides information about the location where the patient is discharged
MARITAL STATUS Marital status of the patient
GENDER Gender of the patient (M:Male, F:Female)
LENGTH OF HOSPITAL STAY DAYS Number of days a patient stays in the hospital
HOSPITAL EXPIRE FLAG Outcome of the hospital stay. Whether a patient is discharged as expired or non-expired. Expired and non-expired outcomes are encoded as 1 and 0, respectively
AGE Patient’s age
HEART_RATE Heart rate
RESPIRATORY RATE Respiratory rate
GCS SCORE Glasgow Coma Scale score (it is related to nervous system)
BLOOD PRESSURE Blood pressure
The objective of this part of the project is that a student will advance his/her learning
in developing statistical models using Excel. Specifically, students will enrich their course
learnings in the following two areas:
1. Hypothesis testing
2. Linear regression
(PROBLEMS TO SOLVE 1-9)
1.3 Problem Statements
Your report should address the following problem statements, and must contain all the
tables, graphs and numeric measures you derive from Excel. Your report must include all
the results for the grading. And please do not forget to add correct labels in X and Y-axes
of your plots.
1. (10 points) As a hospital quality manager, you are interested in investigating if patients
with expired outcomes have the same average length of stay as patients with non-
expired outcomes. For this, you decided to perform hypothesis testing using cuto_
(or critical) value approach. In your excel _le, show all four steps clearly in one sheet.
In your report, write hypothesis formulation, t-statistic, rejection regions and your
conclusion. DO NOT use the hypothesis testing feature of the Data Analysis tool.
Perform all the calculations in Excel.
2. (5 points) Write a linear regression equation to derive the length of stay using the
• Heart rate
• Respiratory rate
• Glasgow Coma Scale score
• Blood pressure
Use B1; B2; B3; B4; B5 and B6 as regression coefficients. We compute value of these coefficients later.
3. (5 points) Using Data Analysis tool, compute the coefficients associated with each
explanatory variable. In your report, create a table and list coefficients of all the
explanatory variables along with their p-values.
4. (4 points) Refer p-values to determine which of the explanatory variables are NOT
5. (4 points) Write down the linear regression incorporating estimated coefficients.
While writing the linear regression equation, use only significantly important explanatory variables and ignore others.
6. (4 points) Explain the interpretation of each coefficients that are significantly important.
7. (5 points) A 64-year patient gets admitted to the ICU in the hospital with Glasgow
Coma Scale score of 6 and blood pressure of 63, what is the predicted length of the
stay of the patient?
8. (6 points) There is a common belief among physicians that the length of stay increases
by 0.5 days with every 1 mmHg increase in blood pressure. Investigate this claim using
9. (3 points) Report the Goodness of Fit measure. What do you think about the model? Is it a good model or poor?
10. (4 points) Clarity of your report.
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