Biostatisticians are constantly called upon to analyze data in order to help researchers and health officials answer critical questions about populations’ health. For this assessment, you will imagine you are a biostatistical consultant on a small study for a local health organization. You have been given the data set provided, along with some background information on how and when the data was collected and the general research question the organization is interested in answering. This is often the way you will receive data in the real world.
Your task is to help the organization answer their question by critically analyzing the data. You will compute your chosen statistics, interpret the results, and present the results and recommendations to non-technical decision makers in the form of a statistical report. Keep in mind that it is your job to do this from a statistical standpoint. Be sure to justify your conclusions and recommendations with appropriate statistical support.

Specifically, you must address the critical elements listed below. Most of the critical elements align with a particular course outcome (shown in brackets).

I. Introduction

1. State the overall health question (To what extent does gender influence length of hospital stay for MI patients?) you have been asked to address in your own words. Be sure you capture the key elements of the question,
   using language that a non-technical audience can understand.
2. Assess the collected data. Use this section to layout the source, parameters, and any limitations of your data. Specifically, you should:

   Describe the key features of your data set. Be sure to assess how these features affect your analysis.
   Analyze the limitations of the data set you were provided and how those limitations might affect your findings. Justify your response.

3. Process: Propose how you will go about answering the health question you were asked to address based on the data set provided.

II. Data Analysis
A. Graphs: In this section, you will use graphical displays to examine the data.

1. Create at least one graph that gives a sense of the potential relationship between the two variables that form your chosen health question. Include the graph and discuss why you selected it as opposed to others.

1. Conduct an appropriate statistical test to answer your health question.
2. Explain why this test is the best choice in this context.
3. Analysis of Biostatistics: Use this section to describe your findings from a statistical standpoint. Be sure to:

   Present key biostatistics from the graph(s) and statistical tests and explain what they mean. Be sure to include a spreadsheet showing your work or a copy of your StatCrunch output as an appendix.
   What statistical inferences or conclusions can you draw based on the results of your statistical test and graph? Justify your response.

III. Conclusions and Recommendations

1. How do the findings help answer your overall health question? Remember to use brief, non-technical language to ensure audience
   understanding.
2. Recommend areas for further research based on your findings. Remember to use brief, non-technical language to ensure audience
   understanding.

Guidelines for Submission: Your statistical report should be approximately 3–5 pages long (including graphs or spreadsheet with calculations), double-spaced, 12-point Times New Roman font, with one-inch margins and citations in APA format. Be sure to use language and a style appropriate for a non-technical audience.

**Attached are completed reports for reference  with data info***

Running head: FINAL PROJECT PART II

1

FINAL PROJECT PART II

7

Final project part II:

Luz Rodriguez

Southern New Hampshire

The statistics shows that over one third of American adults (

3

6

%) are obese and more than two thirds (6

9

%) of the remaining population is overweight. The paper tries to relate the relationship between the hospital stay of the patients as associated with the different BMI of the different patients. This tries to depict the time duration that is associated with the hospital stays among the patients. The BMI has a direct impact to the length of hospital stay. Taking the WHAS1

0

0 data into consideration, the statistics shows the different factors, which are associated with the different trends and the survival rates with respect to the hospital admission for the acute myocardial infarction. This data was taken from all patients that were admitted for MI in the hospital for the time duration stated (Gerstman et al., 201

5

).

The collected data was put under a longitudinal model with 9 different variables within

10

0 different observations. The variables that were put under observation are those that lies between the focus in LOS and the BMI. BMI is the body mass index which be calculated by taking observing the measure and dividing the weights in kilograms and dividing it by the respective heights in meters squared. A longitudinal model was used in generalizing the estimates of the equations and also was used in bringing the analysis and relationship between the BMI and the hospital stay (Hosmer et al. 20

16

).

Summary statistics for los:
Group by: bmi

8

8

8

8

8

8

14

6

6

3

5

1

5

3

4

7

6

4

5

3

8

3

6

On the other hand, the t-test showed two sample T hypothesis test:
Hypothesis test results:

The histogram helps in identifying the gender and the length of stay at the institution. This shows the difference in the groups taking the length of stay and gender as the variables. This shows that women have longer stays in the hospitals. From this analysis, its that the number of women overstaying in the hospitals is more than the number of men who over stay in the hospital (Akinyemiju, 2016, July 18).

References

Gerstman, Burt B. (2015). Basic Biostatistics Statistics for Public Health Practice. Jones & Bartlett Learning. Burlington, MA.

Hosmer, D. W., Lemeshow, S., & May, S. (2016). Applied survival analysis: Regression modeling of time to event data (2nd ed.). New York, NY: John Wiley and Sons Inc.

Akinyemiju, T.-R. N. (2016, July 18). Association between body mass index and in-hospital outcomes. Medicine. Baltimore, Md.: Wolters Kluwer.

IHP 525 Milestone Two Table

A. Assess the collected data. Use this section to layout the source, parameters, and any limitations of your data. Specifically, you should:

1.

Describe the key features of your data set. Be sure to assess how these features affect your analysis.

Summary statistics for los (Length of stay):
Group by: gender

gender

n

Mean

Median

Mode

Min

Max

Std. dev.

0 = Male

65

6.3

5

5

1

17

3.3406011

1 = Female

35

7.8

6

4

3

56

8.9172668

This data set helps in analyzing the observation related from the length of stay in the hospitals and among this patients, the data set shows that 65 are male and the remaining are female. Analyzing the male, their shortest time period was a single day while their longest duration way 17 days. However on the other hand, females took a shorter period of 3 days while the longest time period was 56 days. This shows that females take a longer stay as compared to men with MI.

Analyzing the other parameters, the mean, mode and the median of both male and female that stays in the hospitals and the analysis is as shown in the table above. This would probably give a skewed graph to the right since the mean>median. The standard deviation also shows that the length of stay are closer to the expected average while the case is not the same for the females as in this case it spread out and contains outliers (Lisitsyna et al., 2019).

2. Analyze the limitations of the data set you were provided and how those limitations might affect your findings. Justify your response.

In this case, there are several limitation and this also affects the results differently, this include the fact that the sample size is small, there is no stated reasons why females take longer stays. For this case it’s likely to skew the data. Taking a large sample size may bring up the idea and reasons behind longer stays and if age is a factor in this case. This also does not give a clear picture since the sample size of the male and the female was not the same and the fact that some information regarding the patients is not disclosed which makes it impossible to know if there is any other reason associated with the longer stay and or if environmental condition in the hospital is a factor. All this limitations are likely to impact the results as maybe gender maybe one of the factors that needs to be considered when analyzing hospital stays of MI patients (Sall et al., 2017).

References

Lisitsyna, L. S., & Oreshin, S. A. (2019). Sampling and Analyzing Statistical Data to Predict the Performance of MOOC. In Smart Education and e-Learning 2019 (pp. 77-85). Springer, Singapore.

Sall, J., Stephens, M. L., Lehman, A., & Loring, S. (2017). JMP start statistics: a guide to statistics and data analysis using JMP. Sas Institute.

los 0.0 5.0 10.0 2.0 17.0 33.0 3.0 6.0 1.0

los 0-10 10 to 20 30-40 40-50 50-60 30.0 4.0 0.0 0.0 2.0

IHP 525 Milestone Three Table

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:

Answer:

What is your health (research) question?

To what extent does gender influence length of hospital stay for MI patients?

What is the corresponding null and alternative hypotheses?

Null hypothesis: gender does not influence the length of hospital stay for MI patients.
Alternative hypothesis: gender does influence the length of hospital stay for MI patients

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

Variables are male and female which would be used in determining variance. The graph would depict instance of the MI with respect to both male and female

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

Summary stats chart
Dot-plot

What is the formula for your chosen statistical test?

The summary is used as there is no specified formulae that is used

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 two variables male and female are used in figuring the relationship between the length of hospital stays and gender for the different genders. As much as this does not take into account statistical calculations, this calculations can also be used in the final conclusion. The used test tries to figure out both the variance and summary. The summary and graph used show age length of hospital stay depending on the gender.

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 dot-plot was used in showing a visual reference. A bar graph would also be created to support this information gender would be on the x axis as the dots increases from left to right on the males summary and this statistics varies further when it comes to the women summary.

Step 2: Provide a 1-2 paragraph explanation below as to why you chose the calculations outlined in the table above to explore your health question. Describe what statistics you will compute in order to answer your chosen health (research) question. Be sure to discuss any graphs that you will compute and what information they will provide to help you answer your health question.

In examining the health question and the data that is being computed, it’s evident that this question puts more emphasis on the progressive relationship other than the calculation. To my view I decided that I would determine the instances of MI with relation to the different gender and the length of hospital stays. The health question does not give the relation to each other but rather giving the relationship between gender and the length of hospital patients with MI. with this information, you may determine the statistical calculations which may help in interpreting the instances of MI as per the different gender with their respective percentages. This would be more helpful in supporting the visual graphs which may not have been availed. In disapproving the null hypothesis, the statistics was taken computing the different gender (male and female) (Sadhukhan et al. 2019). This statistical data proved that gender is a variable when it comes to the hospital patient’s diagnosed with MI. the dot plot on the other hand showed increased dots among the female.

References

Sadhukhan, D., Dhar, S., Pal, S., & Mitra, M. (2019). Automated Screening of Myocardial Infarction based on Statistical Analysis of Photoplethysmographic data. IEEE Transactions on Instrumentation and Measurement.

Running head: Final Project Data Analysis 1

Final Project Data Analysis 2

Final Project Data Analysis:

Luz Rodriguez

Southern New Hampshire University

Process and calculations

In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.

Research question

To what extent does gender influence length of hospital stay for MI patients?

Response and predictor variables

Response: Length of hospital stay (LOS)
-Predictor: Gender (female and male)

Type of variable for predictor variable

Predictor: gender (female or male)

Type of diagram for analysis

Dot plot
Histogram

Data analysis

As shown the data tries to compare the differences between gender (male and Female) and the length of stay in hospitals with respect to each other. It’s clear that the length of hospital stay which is represented by 0 is shorter as compared to that of the female which is represented by 1. If there is a larger differences between the two genders, then there is a meaning which would reduce the standard deviation (Gerstman, 2015). 

gender

n

mean

variance

Std. dev

Std. err.

median

range

min

max

Q1

Q3

0

65

0

0

0

0

0

0

0

0

0

0

1

35

1

0

0

0

1

0

1

1

1

1

Hypothesis test results:

Difference

Sample Diff.

Std. Err.

DF

T-Stat

P-value

μ1 – μ2

6.49

0.59375453

198

10.930443

<0.0001

References

Gerstman, B. B. (2015). Basic Biostatistics Statistics for Public Health (2nd ed.). Burlington, MA: Jones & Bartlett Learning.

gender 1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 30.0 4.0 0.0 0.0 0.0 0.0 1.0

gender 0 2.0 17.0 34.0 3.0 6.0 1.0 3.0 gender 1 30.0 4.0 0.0 0.0 0.0 0.0 1.0

gender 0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 2.0 17.0 34.0 3.0 6.0 1.0 3.0