Running head: PHASE 1 SCENARIO NCLEX MEMOORIAL HOSPITAL 1 PHASE 1 SCENARIO NCLEX MEMORIAL HOSPITAL 6 PHASE 1/ Option 2 SCENARIO NCLEX MEMORIAL HOSPITAL Name: Rodney Wheeler Institution: Rasmussen College Course: STA3215 Section 01 Inferential Statistics and Analytics Date: 02/17/17 Introduction The scenario I will be working with is that I am working at NCLEX Memorial Hospital in the infectious disease unit. As a healthcare professional, I need to work to improve the health of individuals, families and communities in various settings. The current situation that has posed as a problem at the hospital and raised eyebrows is that in the past few days, there has been an increase in patients admitted with a particular infectious disease. The basic statistical analysis shows that the disease does not affect minors hence the ages of the infected patients does play a critical role in the method that shall be required to treat the patients in order to impact positively on the health and well-being of the clients being served whether infected with the disease or associated with those infected. After speaking to the manager, we decided that we shall work together in utilising the available statistical analysis to look closer into the ages of the infected patients. To do that, I had to put together a spreadsheet with the data containing the information we shall need to carry out the analysis. Data Analysis From the data collected and input on an Excel sheet, there are sixty patients with the infectious disease. Of the patient’s whose data has already been collected an input on the excel sheet, the ages range from thirty-five years of age to seventy-six. There is only one patient in their thirties with the age of thirty-five. There are five patients in their forties, One forty-five, one forty-six, two at forty-eight and two at forty-nine. There are fifteen patients in their fifties, two at fifty, one fifty-two, one fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at fifty-eight and four at fifty-nine. There are twenty-three patients in their sixties, five at sixty, one at sixty-two, one at sixty-three, two at sixty-four, one at sixty-five, three at sixty-eight and seven at sixty-nine. Finally, we have fifteen infected patients in their seventies, six at seventy, three at seventy-one, three at seventy-two, one at seventy-three, one at seventy-four and one at seventy-six. From the graph in Figure 1 below, the horizontal axis depicts the age group of patients infected with the disease and the vertical axis depicts the number of patients in the age group infected with the disease. Figure 1 Data Classification The qualitative variables in our data analysis would be the names of the patients infected with the disease while the quantitative data would be their ages, number of patients in each age category or age bracket that are infected with the disease and the number of patients in each specific age that are affect ...

1. Running head: PHASE 1 SCENARIO NCLEX MEMOORIAL
HOSPITAL 1
PHASE 1 SCENARIO NCLEX MEMORIAL HOSPITAL
6
PHASE 1/ Option 2 SCENARIO NCLEX MEMORIAL
HOSPITAL
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/17/17
Introduction
The scenario I will be working with is that I am working at
NCLEX Memorial Hospital in the infectious disease unit. As a
healthcare professional, I need to work to improve the health of

2. individuals, families and communities in various settings. The
current situation that has posed as a problem at the hospital and
raised eyebrows is that in the past few days, there has been an
increase in patients admitted with a particular infectious
disease. The basic statistical analysis shows that the disease
does not affect minors hence the ages of the infected patients
does play a critical role in the method that shall be required to
treat the patients in order to impact positively on the health and
well-being of the clients being served whether infected with the
disease or associated with those infected. After speaking to the
manager, we decided that we shall work together in utilising the
available statistical analysis to look closer into the ages of the
infected patients. To do that, I had to put together a spreadsheet
with the data containing the information we shall need to carry
out the analysis.
Data Analysis
From the data collected and input on an Excel sheet, there are
sixty patients with the infectious disease. Of the patient’s whose
data has already been collected an input on the excel sheet, the
ages range from thirty-five years of age to seventy-six. There is
only one patient in their thirties with the age of thirty-five.
There are five patients in their forties, One forty-five, one
forty-six, two at forty-eight and two at forty-nine. There are
fifteen patients in their fifties, two at fifty, one fifty-two, one
fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at
fifty-eight and four at fifty-nine. There are twenty-three
patients in their sixties, five at sixty, one at sixty-two, one at
sixty-three, two at sixty-four, one at sixty-five, three at sixty-
eight and seven at sixty-nine. Finally, we have fifteen infected
patients in their seventies, six at seventy, three at seventy-one,
three at seventy-two, one at seventy-three, one at seventy-four
and one at seventy-six. From the graph in Figure 1 below, the
horizontal axis depicts the age group of patients infected with
the disease and the vertical axis depicts the number of patients
in the age group infected with the disease.
Figure 1

3. Data Classification
The qualitative variables in our data analysis would be the
names of the patients infected with the disease while the
quantitative data would be their ages, number of patients in
each age category or age bracket that are infected with the
disease and the number of patients in each specific age that are
affected. The graph in Figure 1 above shows a quantitative
analysis of the data. The discrete variables in this analysis are
the number of patients infected with the disease because they
could continue to increase to a finite number and we could still
count them and add them to the analysis. Our continuous
variable in this analysis is the age. For our analysis, we shall
use the age in years. In our data set, the qualitative data has
been omitted. The quantitative data is being measured based on
the number of patients counted to have the disease and their
ages. We have classified them in clusters of five in the graph in
order to visualise the analysis. The discrete variable is being
measured by the number of patients already diagnosed with the
diseases and the continuous variable which is the age is
currently being measured annually.
The Measures of Center and Variation
The measures of centre are the values in the middle of the data
set which is the focal point. It can be determined using the mean
medium and the mode. The mean defines the very centre and
could also be defined as the average point. In our data analysis,
it is important to figure out the centre of variation because it
shall assist us to determine the most common age bracket that
has been infected with the disease and shall therefore help us
narrow down to the cause and effect faster by concentrating on
the mean median and the mode of the data analysis.
The measures of variation are those that are utilised to describe
data distribution and the variation between random variable.
They show the range between the greatest and the least data
values which are commonly known as the difference. Quartiles
can be used to measure variation as they divide the data set into

4. four equal parts. They are important as they assist in measuring
probability of occurrence. In our case, they could be used with
the most common age group to have the infectious disease and
random variables such as their residents, their places of work
and their activities or eating habits could be used to further
analyse the data in order to figure out the source, the cure and
the best way to prevent the spread. Arithmetically, it is derived
by the variance and standard deviations of a data set.
Calculation of the Measures of Center and Measures of
Variation
The Mean
The mean is the average of the data set and normally the centre
of the data.
The Mean = Total of Ages / Sample Size
The Mean = 3705 / 60 = 61.81667
The Mean = 61.82
The Median
The Median = The Value in the Centre of the data which in our
case is the value in the centre of the ages. There are 60 patients
hence our median shall be the age of the 30th patient.
The Median = 61
The Midrange
The Midrange = The Midpoint between the lowest and the
highest values. In our data set, the lowest age value is 35 and
the highest is 76
The Midrange = (35+76) /2
The Midrange = 111/2 =55.5
Midrange = 55.5
The Mode
The mode is the most frequent value in the data set. Our data set
is composed of the ages of the infected patients with the
disease. The most frequent age is 69 which has 7 patients
Mode= 69
The Range
The range of a data set is the difference between the highest and
the lowest values in the set. Our data set is composed of the

5. infected patient’s ages. The highest value is 76 and the lowest is
35.
The Range = 76 -35
The Range = 41
The Variance
Measures how far the data are from the mean. In this case the
variance is
4698.9833/60 = 78.3164
The Standard deviation is calculated from the SQRT of the
variance. In this case = 8.85
Conclusion
The conclusion from our study of the patients infected with the
infectious disease in NCLEX Memorial Hospital is that they are
currently sixty. The most infected patients range between the
age of sixty and seventy-five but the highest number of infected
patients are the age of sixty-nine as they are seven. The disease
seems to be attained by the elderly from the age of thirty-five
and seventy-six with the average age being sixty-one. Children,
teenagers, the youth and the extremely elderly are not prone to
the infectious disease.
Infected Patients Graph
"Patients 35-39 40-45 46-49 50-55 56-59
60-65 66-69 70-75 76-80 1 1 5 9
6 12 10 14 1
Running
h
ead:
PHASE 1 SCENARIO
NCLEX MEMOORIAL

6. HOSPITAL
1
PHASE 1/ Option 2
SCENARIO NCLEX MEMORIAL HOSPITAL
Name
:
Rodney Wheeler
Institution: Rasmussen College
Course:
STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/17/17

7. Running head: PHASE 1 SCENARIO NCLEX MEMOORIAL
HOSPITAL 1
PHASE 1/ Option 2 SCENARIO NCLEX MEMORIAL
HOSPITAL
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/17/17
Problem 1
Given this statement:
For all planets in the solar system, there exists a continent on
the planet, such that for all jungles on the content, there exists a
species in the jungle with no teeth or some hair.
a. Write the negation of the statement. (It may be useful to

8. work the problem symbolically, but write your final answer in
words. 6 points.)
b. Unless the original statement is a paradox, either the original
statement or its negation is true. Explain which you believe is
true and why. (Your answer will be evaluated based on the
logic, not the biology. 2 points.)
c. Consider the statement: For all jungles on the continent,
there exists a species in the jungle with no teeth or some hair.
On a continent that has no jungles, can this statement be
declared either true or false? Why? (2 points.)
Running head: COURSE PROJECT –PHASE 3
COURSE PROJECT –PHASE 3
Course Project –Phase 3
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 03/04/17

9. Course Project –Phase 3
The primary goal of statistics is to conduct a hypothesis. A
hypothesis is a prediction about something; hypothesis testing is
done to ascertain if a sampled proportion differs from a
specified population. For the test to be valid eight steps are
conducted to ensure the results are up to par (Lora M. and
Richard J. Cook., 2009);
Step One -Identify and come up with a research question, this
helps the researcher narrow down to what they want to test.For
instance, is the number of patients admitted with infectious
disease less than 65 years of age? Such questions are important
as they help one in looking for the necessary data and conduct
the test efficiently
Step Two-Ascertain that some expectations are met: The method
of research used is Simple random sampling, the resultant
outcome is only one, and the population is triple the sample size
in question
Step Three-State the two types of hypothesis: Identify the null
and alternative hypothesis. Null hypothesis shows equality
while alternative does not.
Step Four-Determine a definite significant level that is the odds
of refuting a null hypothesis through use of alpha
Step Five-Calculate the test statistic, this are constant values
that are calculated from the available data when conducting a
hypothesis test
Step Six-Change the test statistic into a P value; A p-value is

10. the possibility that a selected sample would differ with the
obtained one. It differs depending on the test used and is
determined by use of the normal distribution table
Step Seven-Choose between the null and alternative hypothesis,
this is where one has to determine whether the stated research
question is correct. If the p-value is greater than the
standardized value, the null hypothesis should be rejected
Step Eight-Creating a conclusion of your Research Question,
determine whether or not the set values are sufficient evidence
in confirming your research.
The p-value is the better approach as computation of one value
is required to conduct the test, the critical approach is
cumbersome as one has to compute the test statistic and also
find the key value of the significance level
Question two
1. Ho:p>=65;Ha p<65
2. The test is left tailed since the sample proportion is less than
the hypothesized population proportion
3. The test statistics to be used is the t test since the standard
deviation is unknown.
4. =-2.79
5. Degree of freedom is 60-1=59as observed from the t table the
p- value is 0.05
6. 0.5-0.05=0.45 critical value is -1.6
Subtracting alpha from the standard value of 0.5 then looking
for the resultant difference in the z table.
7. Reject the null hypothesis since the test statistic is less than -
1.6 which is the critical value.
8. There is sufficient evidence that the people who are admitted
in NLEX hospital are less than 65 years of age.

11. References
Giangregorio, Lora M. and Richard J. Cook. "Hypothesis
Testing in Clinical and Basic Science Research". Transfusion,
vol 50, no. 9, 2009, pp. 1878-1880. Wiley-Blackwell,
doi:10.1111/j.1537-2995.2009.02536.x.
Ruxton, Graeme D. and Markus Neuhäuser. "When Should We
Use One-Tailed Hypothesis Testing?". Methods in Ecology and
Evolution, vol 1, no. 2, 2010, pp. 114-117. Wiley-Blackwell,
doi:10.1111/j.2041-210x.2010.00014.x.
Running head
:
COURSE PROJECT
–
PHASE 3

12. Course Project
–
Phase 3
Name
:
Rodney Wheeler
Institution
:
Rasmussen
College
Course:
STA3215 Section 01 Inferential Statistics and Analytics
Date
: 03/04/17

13. Running head: COURSE PROJECT –PHASE 3
Course Project –Phase 3
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 03/04/17
Course Project - Phase 2
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/23/17

14. Importance of constructing confidence intervals for the
population mean
Confidence interval is a range of figures that provides an
interval estimate of a set of unknown parameters (Heckard,
Utts, & Utts, 2012). This is as opposed to using point estimation
and contains the parameter’s value as well as stated probability.
Point estimate, on the other hand, uses a set of sample data to
calculate a statistic (a single value) which serves as the best
estimate of unknown parameter in a population whether random
or fixed (Heckard, Utts, & Utts, 2012).
The best point for the population mean, E(X), is the sample
mean, Xbar. By equating the population mean with the sample
mean, we are solving for the parameters using the one-
parameter case.
Confidence interval (C.I) is needed for bounding the mean and
the standard deviation. In addition, the C.I will also be needed
for obtaining the proportions, regression coefficients and the
differences for the population proportions (Heckard, Utts, &
Utts, 2012). C.I is also needed in obtaining and estimating the
sampling error in relation to the parameter of interest.
Best point estimate for the population mean
The mean is the average of the data set and normally the centre
of the data.
Sample Mean = Total of Ages / Sample Size
Sample Mean = 3705 / 60 = 61.81667

15. Sample Mean () = 61.82
The sample mean is () is the best point estimate of the
population mean (µ).
The best point estimate for the population mean (µ) = 61.82
Confidence intervals for the population mean
Assuming that your data is normally distributed and the
population standard deviation is unknown:
At 95% confident level:
C.I is given by:
With = 61.82, n = 60, s = 8.84597, n-1 = 59
Margin of error =
C.I. = 61.82 1.9083 = (59.9117, 63.7283)
At 99% confident level:
With = 61.82, n = 60, s = 8.84597, n-1 = 59
Margin of error =
C.I. = 61.82 3.0446 = (58.7754, 64.8646)
From the computations above, it can be seen that at 95%
confidence level, the interval of the population mean lies
between 59.9117 and 63.7283. The sample mean is 61.82 and
therefore the mean lies within the interval of the figures. After
increasing the confidence level to 99%, the interval also
increases. At 99% confidence level, the sample mean is still
within the interval as range of the interval figures is 58.7754
and 64.8646.
There are a number of observations that can be made by

16. changing the confidence levels from 95% to 99%. First, the
margin of error increase from 1.9083 to 3.8646. The critical
value read off from the t-table also increases. The confidence
interval also widens as a result of increasing the confidence
interval form 95% to 99%. If the degree of confidence is
increased, it affects the margin of error. The larger the
confidence degree (level), the larger the margin of error
(Heckard, Utts, & Utts, 2012). Since the confidence level is
being increased from 95% to 99%, then the margin of error is
also increased. This ultimately increases the confidence interval
of the population mean.
Reference:
Heckard, R., Utts, J., & Utts, J. (2012). Statistics (1st ed.).
Australia: Brooks/Cole, Cengage Learning.
x
Course Project
-
Phase 2

17. Name:
Rodney Wheeler
Institution: Rasmussen College
Course:
STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/23/17
Course Project - Phase 2
Name: Rodney Wheeler
Institution: Rasmussen College
Course: STA3215 Section 01 Inferential Statistics and Analytics
Date: 02/23/17

18. NamesPatient #Infectious DiseaseAgex-Xbar(x-
xbar)sq1Yes697.1851.60032Yes35-26.82719.13363Yes60-
1.823.30034Yes55-6.8246.46695Yes49-12.82164.26696Yes60-
1.823.30037Yes7210.18103.70038Yes708.1866.96699Yes708.1
866.966910Yes7311.18125.066911Yes686.1838.233612Yes7210
.18103.700313Yes7412.18148.433614Yes697.1851.600315Yes4
6-15.82250.166916Yes48-
13.82190.900317Yes708.1866.966918Yes55-
6.8246.466919Yes49-12.82164.266920Yes60-
1.823.300321Yes7210.18103.700322Yes708.1866.966923Yes76
14.18201.166924Yes56-5.8233.833625Yes59-
2.827.933626Yes642.184.766927Yes719.1884.333628Yes697.1
851.600329Yes55-6.8246.466930Yes61-
0.820.666931Yes708.1866.966932Yes55-6.8246.466933Yes45-
16.82282.800334Yes697.1851.600335Yes54-
7.8261.100336Yes48-13.82190.900337Yes60-
1.823.300338Yes61-0.820.666939Yes50-
11.82139.633640Yes59-2.827.933641Yes60-
1.823.300342Yes620.180.033643Yes631.181.400344Yes53-
8.8277.733645Yes642.184.766946Yes50-
11.82139.633647Yes697.1851.600348Yes52-
9.8296.366949Yes686.1838.233650Yes708.1866.966951Yes697
.1851.600352Yes59-2.827.933653Yes58-
3.8214.566954Yes697.1851.600355Yes653.1810.133656Yes61-
0.820.666957Yes59-
2.827.933658Yes719.1884.333659Yes719.1884.333660Yes686.
1838.233637094698.983361.82Variance 78.3164S. D
8.8496547328