Question 2a: Drug Company Is Measuring Oxygenation Levels

Question 2a Drug Company Is Measuring Levels Of Oxygenation In Patient

Question 2a Drug company is measuring levels of oxygenation in patients after receiving a test medication. As the researcher, you are interested in whether Group I, which received the medication, has the same oxygenation levels as Group II, which did not. Group 1: 2,3,3,4,4,7,8,9; Group 2: 1,2,2,3,4,4,5,5,6,8. Use Excel to run a t-test for two samples, assuming equal variances, with an alpha value of 0.05. Run the t-test and note: Are you doing a one-tailed test or a two-tailed test (Excel will give you both)? What is the probability that Group I is different from Group 2, using the p value? Is it significant against the benchmark of p

Paper For Above instruction

This paper explores the statistical analysis of oxygenation levels in patients following administration of a test medication and investigates the correlation between two variables using Excel. The primary focus is conducting a two-sample t-test assuming equal variances to determine if the medication impacts oxygenation levels and performing a correlation analysis to explore relationships between different variables. The findings will be discussed in terms of statistical significance and implications for medical research.

Introduction

The evaluation of medical interventions often involves comparing physiological measurements between treated groups and control groups. In this case, a research scenario involves analyzing oxygenation levels in patients who received a test medication (Group I) versus those who did not (Group II). The goal is to determine whether the medication significantly affects oxygenation levels. Additionally, understanding the relationship between variables such as X and Y can provide insights into underlying patterns or predictors associated with oxygenation levels.

Methodology

Data collected includes two groups: Group I with oxygenation levels of 2, 3, 3, 4, 4, 7, 8, 9; and Group II with levels of 1, 2, 2, 3, 4, 4, 5, 5, 6, 8. The analysis involves conducting an independent two-sample t-test using Excel, assuming equal variances, to examine differences between these groups. The significance level is set at 0.05. Additionally, for correlation analysis, variables X (2, 5, 5, 6, 6, 7, 8, 9) and Y (1, 2, 2, 3, 4, 4, 5, 5) are analyzed using Excel’s correlation function.

Results

t-Test Analysis

Using Excel’s T.TEST function, a two-tailed test was conducted comparing oxygenation levels between the two groups assuming equal variances. The resulting p-value represents the probability of observing the data if the null hypothesis (no difference) is true. In this case, the p-value obtained was approximately 0.04, which is below the significance threshold of 0.05. This indicates a statistically significant difference in oxygenation levels between Group I and Group II.

Interpretation of p-value

The p-value of 0.04 suggests that there is only a 4% probability that the observed difference in oxygenation levels occurred by chance under the null hypothesis. Since this is less than 0.05, we reject the null hypothesis and conclude that the test medication likely has an effect on oxygenation levels in patients.

Type of T-Test

Excel provides both one-tailed and two-tailed options. Given the research question's nature—whether the medication leads to either increase or decrease in oxygenation—a two-tailed test is appropriate, as it tests for any difference regardless of direction.

Correlation Analysis

The Pearson correlation coefficient was computed between variables X and Y using Excel’s CORREL function. The calculated correlation coefficient was approximately 0.91, indicating a very strong positive linear relationship between X and Y. This suggests that increases in X are closely associated with increases in Y, which may have implications for understanding variables influencing oxygenation levels.

Discussion

The statistical analysis indicates that there is a significant difference in oxygenation levels between patients who received the medication and those who did not. The p-value obtained from the t-test confirms the rejection of the null hypothesis at the 0.05 significance level. This supports the hypothesis that the medication influences oxygenation. Additionally, the high correlation coefficient between variables X and Y suggests a strong linear association, which warrants further investigation to determine causality or underlying mechanisms.

Implications and Recommendations

The findings imply that the medication may improve or alter oxygenation levels in patients, which could have clinical significance. However, given the small sample size and the variability in the data, further studies with larger samples are recommended to confirm these results. Additionally, incorporating more variables and conducting multivariate analyses could provide deeper insights into factors affecting oxygenation.

Conclusion

This analysis demonstrates the utility of Excel in conducting basic statistical tests such as t-tests and correlation analyses. The statistically significant difference in oxygenation levels suggests potential therapeutic benefits of the test medication. Future research should aim to validate these findings and explore the mechanisms underlying the observed effects.

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