A researcher is conducting a study on the effects of different exercise regimens on blood pressure. The study involves $100$ participants

who are randomly assigned to one of three exercise groups: jogging, weightlifting, or yoga. Each participant's blood pressure is

measured before and after the $6-$week exercise program.

The researcher has collected the data and stored it in a CSV file. The file contains the following columns:

Participant ID $($numeric$)$

Exercise group $($text: "jogging," "weightlifting," or "yoga"$)$

Pre$-$exercise systolic blood pressure $($numeric$)$

Post$-$exercise systolic blood pressure $($numeric$)$

The researcher wants to analyze the data using Python and NumPy. Complete the following tasks as part of the initial statistical analysis

of the scenario above.

Tasks:

Generate Synthetic Dataset on Exercise and Blood Pressure

Create a Python script that generates a synthetic dataset matching the description of your study. The dataset should be saved as a

CSV file named "exercise$\_$data.csv$".$

Highest Pre$-$Exercise Blood Pressure by Group

Write a Python script to read the "exercise$\_$data.csv$"$ file and print the participant with the highest pre$-$exercise systolic blood

pressure in each exercise group.

Extract the $5$ Participants with Highest Blood Pressure

Write a Python function that sorts the list based on blood pressure and displays the full record of the top $5.$

Monthly Blood Pressure Changes

Write a Python script that assumes that blood pressure measurements were taken monthly. Compute and print the average change

in blood pressure for each exercise group. Note: This is hypothetical as the original study is for $6$ weeks only.

Compare Pre$-$ and Post$-$Exercise Blood Pressure

Search for the $5$ participants from the pre$-$exercise $($Topic $4)$ and find their post$-$exercise blood pressure. Produce a table that

compares their pre$-$ and post$-$exercise pressure and displays the difference.

Total Blood Pressure Reduction for Each Exercise Group

Write a Python script to read the "exercise$\_$data.csv$"$ file and compute the measures of central tendency for each exercise group:

mean, mode, standard deviation.

Explain in detail the computational and statistical aspects of each task above. For example, the researcher may use NumPy to calculate

the mean and standard deviation of the pre$-$exercise and post$-$exercise blood pressure measurements for each exercise group. The

researcher may also use NumPy to calculate the difference between the pre$-$and post$-$exercise blood pressure measurements for each

participant and to create a new array that contains only the differences. The researcher may use Python's control flow and logical

operators to filter the data and select only the participants who had a decrease in blood pressure after the exercise program. The

researcher may use NumPy to calculate the mean and standard deviation of the blood pressure differences for this subgroup and draw

conclusions about the effectiveness of each exercise regimen in reducing blood pressure. Finally, a useful and informative task is

completed.