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 Course ProjectCriteriaPointsDescribes the patient that is the subject of the project including diagnoses, medications, and history OR describes the community, its strengths and problems and the mental health issue that will be the subject of the paper.4Includes any substance abuse or violence issues for the patient or community 2Discusses attempted interventions, what has been successful and what has not.4Describes own personal thoughts about the patient's or community's mental health issues. 4Describes any cognitive concerns and possible interventions.2Writes a nursing care plan including three priority nursing diagnoses with r/t and AEB factors.4Includes outcomes in Nursing Outcomes Classification language and interventions in Nursing Intervention Classificati ...

1. 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)

2. 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

3. 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

4. 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
Course ProjectCriteriaPointsDescribes the patient that is the
subject of the project including diagnoses, medications, and
history OR describes the community, its strengths and problems
and the mental health issue that will be the subject of the
paper.4Includes any substance abuse or violence issues for the
patient or community 2Discusses attempted interventions, what
has been successful and what has not.4Describes own personal
thoughts about the patient's or community's mental health

5. issues. 4Describes any cognitive concerns and possible
interventions.2Writes a nursing care plan including three
priority nursing diagnoses with r/t and AEB factors.4Includes
outcomes in Nursing Outcomes Classification language and
interventions in Nursing Intervention Classification language
with a plan for evaluation.4Identifies mental health resources
that can be used with the patient or community. Includes an
educational tool.4Supports answers using academic resources,
with citations in APA format.2Total30
1
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.

6. 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

7. 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 PART II 1
FINAL PROJECT PART II 7

8. Final project part II:
Luz Rodriguez
Southern New Hampshire
The statistics shows that over one third of American adults
(36%) are obese and more than two thirds (69%) 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 WHAS100 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., 2015).
The collected data was put under a longitudinal model with 9
different variables within 100 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. 2016).

9. Summary statistics for los:
Group by: bmi
BMI
n
Mean
Variance
Std. dev.
Std. err.
Median
Range
min
Max
Q1
Q3
10- 15
1
8
8
0
8
8
8
8
15- 20
6
8
14
3.7416574
1.5275252
7.5
11
3
14

10. 6
10
20-25
29
7.1034483
10.310345
3.2109726
0.59626264
6
13
3
16
5
9
25-30
35
7.3428571
82.408403
9.0779074
1.5344464
5
55
1
56
4
7
30- 35
23
5.9565217
10.043478
3.1691447
0.66081235
5
15
3
18

11. 4
7
35 to 40
6
4.6666667
3.8666667
1.9663842
0.80277297
4
5
3
8
3
6
On the other hand, the t-test showed two sample T hypothesis
test:
Hypothesis test results:
Difference
Sample Diff.
Std. Err.
DF
T-Stat
P-value
μ1 - μ2
6.49
0.59375453
198
10.930443
<0.0001
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

12. 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
Information on data set
Which variables are you investigating?
Length of hospital stay based on gender
What is the type of each variable?

13. Length of stay is quantitative
Gender is category
List the descriptive stats you will run on the data.
Distribution, Mean, Medium, Mode, Minimum, Maximum, &
Standard Deviation
What does each calculation tell you about the data?
Distribution describes the shape / how the data trends when
graphed
Mean = the average
Mode = the value that appears the most
Median = The midpoint of the data
Minimum = The lowest value observed
Maximum = The highest value observed
Standard Deviation = the amount of variation present among the
data values
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

14. 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.

15. 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