2. (20 points) It is very important that treatments are developed to save the lives of those who are critically ill from COVID-19. Critically ill patients often require mechanical ventilators, which use positive pressure to assist or push a breath into the lungs. Pressure Support is a common type of breathing support used when a patient needs partial support. The amount of Pressure Support delivered is measured in cm H₂O and ranges between 5 (minimal support) and 30 (total support). Source: London Health Sciences Centre. Suppose that a new treatment is being tested and a simple random sample of 12 patients who are on mechanical ventilators and receive the treatment are selected. The following table shows the Pressure Support levels, in cm H₂O, required before the treatment and two days after the treatment for the 12 different patients. Patient Pressure Support Before Treatment Pressure Support After Treatment Difference d A B 28 20 26 20 C 21 16 D 18 21 E 24 21 F 30 30 G 9 6 H 15 12 I 24 22 J 17 11 K 20 20 L 11 7 Use a 0.01 significance level to test the claim that there is a reduction in Pressure Support (and hence an improvement in condition) using this new treatment. Assume that the differences have a normal distribution.
2. (20 points) It is very important that treatments are developed to save the lives of those who are critically ill from COVID-19. Critically ill patients often require mechanical ventilators, which use positive pressure to assist or push a breath into the lungs. Pressure Support is a common type of breathing support used when a patient needs partial support. The amount of Pressure Support delivered is measured in cm H₂O and ranges between 5 (minimal support) and 30 (total support). Source: London Health Sciences Centre. Suppose that a new treatment is being tested and a simple random sample of 12 patients who are on mechanical ventilators and receive the treatment are selected. The following table shows the Pressure Support levels, in cm H₂O, required before the treatment and two days after the treatment for the 12 different patients. Patient Pressure Support Before Treatment Pressure Support After Treatment Difference d A B 28 20 26 20 C 21 16 D 18 21 E 24 21 F 30 30 G 9 6 H 15 12 I 24 22 J 17 11 K 20 20 L 11 7 Use a 0.01 significance level to test the claim that there is a reduction in Pressure Support (and hence an improvement in condition) using this new treatment. Assume that the differences have a normal distribution.
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Transcribed Image Text:### Exploring New Treatments for COVID-19: Pressure Support Analysis
#### Introduction
In the pursuit of treatments for critically ill COVID-19 patients, one promising area of research is the improvement of mechanical ventilation strategies. Critically ill individuals often require ventilators that provide a positive pressure to aid breathing. Pressure Support is a vital form of assistance provided in these scenarios, measured in centimeters of water (cm H₂O), and typically ranges from 5 (minimal support) to 30 (total support).
#### Study Overview
A recent study conducted by the *London Health Sciences Centre* aims to evaluate the efficacy of a new treatment designed to reduce the required Pressure Support for patients on ventilators. The study involved a simple random sample of 12 patients, measuring their Pressure Support levels before and two days after treatment.
#### Data Summary
The table below details the Pressure Support levels for each patient before and after the treatment, along with the computed differences.
| Patient | A | B | C | D | E | F | G | H | I | J | K | L |
|---------|----|----|----|----|----|----|----|----|----|----|----|----|
| Before | 28 | 20 | 21 | 18 | 24 | 30 | 9 | 15 | 24 | 17 | 20 | 11 |
| After | 26 | 16 | 21 | 21 | 30 | 6 | 12 | 12 | 21 | 20 | 20 | 7 |
| Diff. d | | | | | | | | | | | | |
#### Statistical Analysis
The goal is to test the hypothesis that there is a significant reduction in Pressure Support post-treatment, indicative of improved patient condition. This is done using a significance level of 0.01, assuming a normal distribution of differences.
***Note:*** Calculate the differences (before treatment - after treatment) and perform statistical tests to determine the significance of the results.
#### Conclusion
Through rigorous analysis, the study seeks to validate the new treatment's potential in reducing breathing support needs, thus aiding patient recovery. Further research and trials could cement its role in COVID-19 patient care.
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