Execute the following two-sided hypothesis using a t-test and the appendicitis.xlsx dataset: : H0 Average LOS per episode is the same for individuals admitted with complicated versus uncomplicated appendicitis. : H1 Not H0. 2. Turn in a structured abstract (no more than 300 words, not including the section headers, so, 307 in total) using the format shown below. It should be no longer than two pages (one usually suffices). a. A point will be deducted if you exceed the word limit. Adhering to the word limit is essential as it forces you to get to the point; at the same time, be careful not to omit anything of importance. i. Do not include tables or graphs in the abstract (that would go in the main text or presentation - if you were to do one)! Determine which values from your test(s) are most important to the reader and report those in the abstract. ii. Since the word limit applies to everyone equally, no exceptions will be made!!! b. What is important or relevant information is by definition, at least partially, subjective. However, there are certain rules. i. For example, in the results section you should discuss the significance of your test-statistic. If you do not, at least one point will be deducted (yes, this is a big oversight). ii. Given the nature of the hypothesis and the variables involved, you should discuss, in a sentence, the difference between complicated versus non-complicated appendicitis (just use any source on the internet for this). Indicate what proportion of the sample was complicated versus not. c. Special importance is attached to the discussion section of the abstract (see below). This is where you have to apply your creativity as a story teller. This is where you help make information out of data as it involves the reader. Try to convey why the hypothesis and the analysis are important (if you personally don't care, ask yourself why anyone would). Imagine how and what decisions could be affected by the analysis and findings and communicate this to your intended audience. Simple repetitive statements already expressed elsewhere in the abstract will result in deductions, so do not just repeat the conclusions! This is a generic example of a structured abstract. Title: A Comparison of profit versus not-for-profit Abstract Objective: This contains a brief statement describing the general objective of the analysis. For example, "This analysis examines the number of hospital bed days devoted to uninsured patients by not-for-profit versus for-profit institutions." Data and methods: This contains a brief description of the dataset used to test the hypothesis. For example, concerning the dataset briefly mention the unit of measurement, whether the data is publicly available, and what types of variables it includes. Concerning the method, discuss whether you used a two-sample t-test assuming equal variances or not. Results: This contains a brief description of the t-statistic you generated and its p-value. Since the test involves two samples (and a pooled sample) you may want to first discuss the descriptive statistics for the sub-samples and the combined sample (i.e. the mean, since that is what you are analyzing). You should also mention the number of observations in each category. Conclusions: A very brief statement indicating your conclusions pertaining to the hypothesis - did your results reject the null hypothesis or not? Discussion: This is perhaps the most important section. Here you tell the story of why you believe the analysis was important - why is it "information" and not just "data"? Why would anyone within related professional or policy circles find your analysis and results interesting? How could your analysis help such individuals in terms of decision making? Word Count (not including the section headers): 219 3. What to turn in! a. Your abstract! Complete it before accessing the assignment on canvas, then upload your word or rtf file using question #3. b. Supporting materials! On the second page (be sure to insert a page break) right below the abstract in the same document, create a section titled "Appendix: Supporting Materials." For example, in the appendix, you should copy and paste the test results. Notice in the example below that I formatted the column widths so make everything fit without encroachment of text from one column onto another. Notice also that I formatted the numbers: for example, when values get as large as the variances reported in the table, there is typically no need for anything behind the decimal point. Remember, as was emphasized in assignment 3, presentation is important. Do not overload the reader!

PLEASE amend my version below to match the instructions and reduce the wording to fit the requested number in the assignment above for master-level students.

Title: Analysis of Length of Stay for Uncomplicated versus Complicated Appendicitis Cases

Abstract: This analysis investigates the Length of Stay (LOS) for uncomplicated and complicated appendicitis cases to ascertain whether there exists a significant discrepancy in hospital bed days between these patient groups.

Data and Methods: In the analysis, Length of Stay (LOS) data for uncomplicated and complicated patient cases was drawn from a dataset of 33,067 uncomplicated cases and 14,391 complicated cases, measured in hospital bed days. The dataset was considered proprietary; however, for individuals admitted with complicated versus uncomplicated appendicitis, data is publicly available. The Centers for Medicare and Medicaid Services (CMS) publish unadjusted inpatient claims data containing codes for diagnosis and complications. Moreover, additional sources like the American College of Surgeons offer data on outcomes and complications of appendicitis surgeries through various research activities. To compare the mean LOS between the two groups, a two-sample t-test, specifically Welch's t-test, was utilized to cater to any potential variance differences between uncomplicated and complicated cases effectively. This method ensured a robust statistical comparison to determine the significant difference in LOS between the two groups.

Results: Descriptive statistics revealed that the mean Length of Stay (LOS) for uncomplicated cases is 1.93 days with a variance of 3.42, while for complicated cases, the mean LOS is 5.53 days with a variance of 22.14. The F-test result (F = 0.1542, p = 0) indicated unequal variances. Subsequently, the two-sample t-test was conducted using Welch's t-test, resulting in a t-value of -88.90 with degrees of freedom = 16,354 and p-value = 0. This significant t-value suggests a substantial difference in mean LOS betweenthe two groups.

Conclusions: The rejection of the null hypothesis suggests a notable distinction in the mean LOS between uncomplicated and complicated appendicitis cases.

Discussion: This analysis underscores the disparity in hospital resource usage between uncomplicated and complicated cases, offering insights beneficial for optimized resource allocation and enhanced patient care management. The substantial LOS difference emphasizes the need for tailored interventions and resource strategies for complicated cases to potentially minimize hospital stays and associated expenses

	LOS-Uncomplicated	LOS-Complicated
Mean	1.93	5.53
Variance	3.42	22.14
Observations	33067	14391
df	33066	14390
F	0.1542
P(F<=f) one-tail	0
F Critical one-tail	0.9771
t-Test: Two-Sample Assuming Unequal Variances
	LOS-Uncomplicated	LOS-Complicated
Mean	1.93	5.53
Variance	3.42	22.14
Observations	33067	14391
Hypothesized Mean Difference	0
df	16354
t Stat	-88.90
P(T<=t) one-tail	0
t Critical one-tail	1.64
P(T<=t) two-tail	0
t Critical two-tail	1.96