IHP 525 Milestone Three Table For This Milestone In Order

Ihp 525 Milestone Three Tablefor This Milestone In Order To Explore Y

For this milestone, in order to explore your health question you are investigating, you need to plan what descriptive statistics and statistical test you will need to run, as well as what graph you will need to create. Step 1: Complete the table below in which you will propose the calculations and graph(s) you will need to perform to answer the health question you are investigating.

Question: What is your health (research) question? To what extent does gender influence the length of hospital stays for MI patients?

Answer: The corresponding null and alternative hypotheses are:

• H0: There is no difference in length of stay for MI patients between gender.
• Ha: There is a statistically difference in length of stay for MI patients between gender.

List the descriptive statistics you will compute, using which variable(s), to help answer your health question.

• The mean of number of days spent in hospital for males and females.
• Standard deviation for number of days spent in hospital for males and females.

What is the name of the statistical test you will use to test your hypothesis and answer your health question?

Independent samples t-test

What is the formula for your chosen statistical test?

T =

Why is the statistical test you chose appropriate to answer your health question? Be sure to be clear on how the two variables you described in Milestone Two are used to complete this test.

The independent samples t-test is appropriate because it compares the means of a continuous variable (length of stay) between two groups (gender). Specifically, the test evaluates whether the difference in average hospital stay length between males and females is statistically significant, using the calculated means and standard deviations for each group.

Which graph(s) (histogram, stem and leaf, boxplot, bar graph, scatterplot) will you use to visualize the answer to your health question? Be specific and include which variables will be used and if the graph will be created for different subgroups of subjects.

A bar graph will be appropriate where gender will be represented on the x-axis and the average number of hospital days on the y-axis, allowing visual comparison between males and females.

Paper For Above instruction

Understanding the influence of gender on the length of hospital stays among myocardial infarction (MI) patients is crucial for healthcare planning and resource allocation. This analysis employs descriptive and inferential statistics to examine whether significant differences exist between male and female patients regarding their hospital stay durations. The following discussion delineates the statistical approach to investigate this research question, emphasizing the choice of methods, their justification, and interpretation of results.

To explore the potential impact of gender on hospital stay length, the initial step involves descriptive statistics. Calculating the mean and standard deviation of hospital days separately for male and female patients provides basic insights into the central tendency and variability within each group. The mean offers an average estimate of hospital stay, while the standard deviation indicates dispersion, which can highlight heterogeneity in patient experiences. These statistics serve as foundational metrics to identify preliminary differences that may warrant further hypothesis testing.

The primary inferential method employed is the independent samples t-test. This statistical test compares the means of two independent groups (males and females) on a continuous variable (length of hospital stay). The t-test formula, T = (X̄₁ - X̄₂) / √(s₁²/n₁ + s₂²/n₂), involves the difference between group means, pooled standard deviations, and sample sizes. This calculation yields a t-statistic that quantifies how much the observed difference deviates from the null hypothesis of no difference, scaled by variability and sample size.

The choice of the independent samples t-test is justified because it is specifically designed for situations where the dependent variable is continuous, and the independent variable categorically splits the sample into two groups. In this context, the continuous variable is the length of hospital stay, and the grouping variable is gender. The test assumes that the data are approximately normally distributed within each group and that variances are equal or similar. If these assumptions are violated, alternative methods such as the Welch's t-test or non-parametric tests may be considered. However, for most practical purposes in medical research, the independent t-test provides an appropriate and straightforward analysis.

Visualization of the data complements the statistical analysis by providing an intuitive understanding of group differences. A bar graph depicting the mean hospital stay for males and females allows easy comparison and can visually reveal the magnitude of differences, if any. The graph should include error bars representing confidence intervals or standard error to illustrate variability and statistical uncertainty.

In conclusion, the combination of descriptive statistics, a well-justified t-test, and graphical visualization supplies a comprehensive approach to assessing whether gender influences hospital stay duration among MI patients. These methods facilitate rigorous statistical inference and support evidence-based decision-making in healthcare settings.

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