Running Head: SUN COAST HEALTH 2Evaluating Health and Safety at Sun Coast: A Quantitative Analysis

Running head: RESEARCH METHODOLOGY, DESIGN AND
METHODS 1
RESEARCH METHODOLOGY, DESIGN AND METHODS 5
Insert Title Here
Insert Your Name Here
Insert University Here
Research Methodology, Design, and Methods
Research methodology refers to the precise procedures that
researchers select base on the chosen design. Details of how
data is collected and analyzed is presented here. The research
that led to the achievement of Sun Coast objectives was done
using quantitative research methods since they offer detailed
insights pertaining to the study. Research design is the specific
type of study that one would conduct and is usually consistent
with one’s philosophical worldview and the methodological
approach the researcher chooses (Creswell & Creswell, 2018).
This study seeks to evaluate the research methodology, design,
and methods used in researching the concerns in Sun Coast
leadership in business context.
Research Methodology
This research needed to involve much details from the
respondents and data collected. Hence, quantitative research
methodology was chosen. For instance, Sun Coast leadership
and other business contexts involved numbers which needed to
be analyzed in details rendering
Research Design

The research design employed in this research was causal one
based on various reasons. Firstly, this design is quantitative in
nature and it is also preplanned in design (Creswell & Creswell,
2018). Hence, it is considered as a conclusive research which is
in line with Sun Coast where leadership trend was monitored
against its business performance. Also, researching the
performance of this organization required an explanation off
cause and effect relationships that variables have. Hence, causal
research design would aid in understanding the variables that
causative and those which acts as effects of leadership and
performance noted in Sun Coast.
Research Methods
This research applied the causal-comparative research methods
which was sometimes combined with the descriptive statistics
one (Creswell & Creswell, 2018). The former was used to find
the relationship between dependent and independent variables
after the occurrence of any action in Sun Coast. For instance,
this method was useful in determining whether the independent
or dependent variable affected the outcome through comparison
of at least 2 groups of data collected. On the other hand, Sun
Coast’s leadership and other business objectives could render
descriptive statistics significant since the researchers could use
the past figures to analyze the current ones and make a sound
forecast of future organizational performance. Hence, the two
research methods was sometimes combined to get a more
credible conclusion based on data at hand.
Data Collection Methods
Different data collection methods including surveys,
observation, and interviews were used to test each hypothesis
(Creswell & Creswell, 2018). Observation was used for real life
situations where pertinent happenings were then recorded. This
data collection method were used in testing the hypotheses, ‘the
blood lead levels have increased considering the sampled
employees.’ Surveying was done to analyze the distributed data
and come up with a logical conclusion. This method was used to

test the hypotheses such as, the statistical relationship between
the PM size and the worker’s health and the difference between
return on investment exist considering four lines of service that
Sun Coast offers to their customers. Interviews involved taking
the respondents’ opinions pertaining to the research questions
developed (Creswell & Creswell, 2018). This method was used
to test the hypothesis, the efficiency of the revised programs
compared to the prior training one.
Sampling Design
The sampling design that was most likely used for this research
was the stratified one which involved dividing populations in
Sun Coast into groups based on factors such as those who
attended revised training versus those who did not among other
groups. After developing groups, a probability sample was then
selected and grouped into strata based on their positions in Sun
Coast and what they believe in terms of leadership.
Data Analysis Procedures
Correlation is the preferred procedure to use to test the RQ1
hypotheses since the interest is whether a relationship exists
between an independent variable (IV) and dependent variable
(DV). Correlation will indicate if there is a relationship between
PM size (IV) and the employee health (DV) and the magnitude
of that impact if at all there is one.
Correlation is also appropriate in testing the RQ2 since the
question aims to learn the relationship between training about
health and safety and time wasted in attending the services. This
procedure will indicate the relationship between the two
variables considering that the latter is a DV while its former
counterpart is independent.
Regression analysis procedure would be appropriate for RQ3
since the variable, DB levels of work would be predicted before
placing employees on-site for future contracts. There is no
independent sample among those provided by this RQ. ANOVA
procedure would be suitable for testing RQ4 and RQ5 since the
variables in the two research questions are non-directional. The
former tests the efficiency between two groups of employees

while the latter tests the blood sugar levels among the chosen
employees.
References
Creswell, J. W., & Creswell, J. D. (2018). Research design:
Qualitative, quantitative, and mixed methods approaches (5th
ed.). Thousand Oaks, CA: Sage.
Smart Tree
A Solar tree is a decorative means of producing solar energy
and also electricity. It uses multiple no of solar panels which
forms the shape of a tree. The panels are arranged in a tree
fashion in a tall tower/pole.
At the top of the pole will be a CCTV, around the tree will be
Thermal/ Humidity sensors to detect the temperature in the
surroundings. To the Power output battery there will be smart
wireless switches connected.
Topic is about the Smart Tree (Solar Tree) Which is my
Implementation time now. The Smart Tree has few features like:
· CCTV.
· IoT smart Wireless switch.
· Thermal & Humidity Sensor.
a. CCTV for security purpose storing the data and can view the
recordings.
b. IoT smart switch is about the tree which will be capable of
controlling the surrounding temperatures like automatically
switching on the sprinklers around and cool the surroundings
until the temperature is decreased.
So, this tree also keeps the CCTV footage for safety issues.

It can also store the Temperature log throughout so that to turn
the sprinklers on by itself.
1. Introduction Smart Tree (Solar Tree)
· Background (A bit of work on the smart tree similar
background)
· Aim of Research / Problem Formulation
· Approach of Research
· Objectives (What should be the objectives)
· Scope of Research.
2. Literature Review
· How smart tree is useful ion the community.
· What are the advantages?
· Comparison of another tree with ours.
· Countermeasures.
3. Chapter covering approach
4. Analysis and Discussion
· Design
· Uses in University.
· Environment conditions.
· Framework.
· Results.
5. Conclusion.
6. Future Works.
Word Count is 3,500.
Running Head: SUN COAST HEALTH 2
SUN COAST HEALTH 2
Sun Coast Health
By

Name
Date
Table of Contents
CHAPTER ONE 5
1.1 Introduction 5
1.2 Statement of the Problems/Dilemmas 5
1.2.1 Particulate Matter (PM) 5
1.2.2 Safety Training Effectiveness 6
1.2.3 Sound-Level Exposure 6
1.2.4 New Employee Training 6
1.2.5 Lead Exposure7
1.2.6 Return-On-Investment 7
1.3 Research Objectives 7
1.4 Research Questions and Hypotheses 8
CHAPTER TWO 11
2.1 Literature Review 11
2.2 Conceptual framework 11
CHAPTER THREE 12
RESEARCH METHODOLOGY, DESIGN, AND METHODS 12
3.1 Introduction 12
3.2 Research Design 12
3.3 Research Methods 13
3.4 Data Collection Methods 13
3.5 Sampling Design 13
3.6 Questionnaires 13
3.7 Data Analysis Procedures 14

3.8 Data Analysis 14
3.8.1 Correlation Analysis 15
3.8.2 Simple Regression Analysis 16
3.8.3 Multiple Regression Analysis 17
3.8.4 Independent Samples t Test 18
3.8.5 Paired Samples t Test 18
3.8.6 One-Way ANOVA 19
CHAPTER FOUR 20
RESEARCH FINDINGS AND DISCUSSIONS 20
4.1 Introduction 20
4.2 Summary of key findings 20
CHAPTER FIVE 21
SUMMARY CONCLUSION AND RECOMMENDATION 21
5.0 Introduction 21
5.1 Recommendations 22
5.2 Conclusion 22
5.3 Suggestion for Further research 23
References 24
CHAPTER ONE1.1 Introduction
Senior leadership at Sun Coast has identified several areas for
concern that they believe could be solved using business
research methods. The previous director was tasked with
conducting research to help provide information to make
decisions about these issues. Although data were collected, the

project was never completed (Creswell & Creswell, 2018).
Senior leadership is interested in seeing the project through to
fruition. The following is the completion of that project, and
includes statement of the dilemmas, literature review, purpose
statements, research methodology, design, and methods,
research questions and hypotheses, data analysis, and findings.
1.2 Statement of the Problems/Dilemmas
1.2.1 Particulate Matter (PM)
There is a concern that job-site particle pollution is adversely
impacting employee health. Although respirators are required in
certain environments, particulate matter (PM) varies in size
depending on the project and job site. PM between 10 and 2.5
microns can float in the air for minutes to hours (e.g. asbestos,
mold spores, pollen, cement dust, fly ash), while PM less than
2.5 microns can float in the air for hours to weeks (e.g. bacteria,
viruses, oil smoke, smog, soot) (Ferreira, 2011). Due to the
smaller size of PM less than 2.5 microns, it is potentially more
harmful than PM between 10 and 2.5 since the conditions are
more suitable for inhalation. PM less than 2.5 are also able to
be inhaled into the deeper regions of the lungs, potentially
causing more deleterious health effects. It would be helpful to
understand if there is a relationship between PM size and
employee health. Air quality data have been collected from 103
job sites, which is reflected in PM size. Data is also available
for average annual sick days per employee per jobsite.
1.2.2 Safety Training Effectiveness
Health and Safety training is conducted for each new contract
that is awarded to Sun Coast. Data for training expenditures and
lost-time hours were collected from 223 contracts. It would be
valuable to know if training has been successful in reducing
lost-time hours and, if so, how to predict lost-time hours from
training expenditures (Wang et al., 2009).
1.2.3 Sound-Level Exposure

Sun Coast’s contracts generally involve work in noisy
environments due to a variety of heavy equipment being used
for both remediation and the clients’ ongoing operations on the
job sites. Standard earplugs are adequate to protect employee
hearing if the decibel levels are less than 120 decibels (dB)
(Creswell & Creswell, 2018). For environments with noise-
levels exceeding 120 dB, more advanced and expensive hearing
protection is required, such as ear-muffs (Ferreira, 2011). Data
have been collected for the primary variables that are believed
to contribute to excessive noise. It would be important if these
data could be used to predict the dB levels of work
environments before placing employees on site.
1.2.4 New Employee Training
All new Sun Coast employees participate in general health and
safety training. The training program was revamped and
implemented six months ago. Data is available for two Groups;
a) Group A employees who participated in the previous training
program, and b) Group B employees who participated in the
revised training program. It is necessary to know if the revised
training program is more effective than the prior training
program.
1.2.5 Lead Exposure
Employees working on job sites to remediate lead must be
monitored. Lead levels in blood are measured as micrograms of
lead per deciliter of blood (μg/dL). A base-line is taken pre-
exposure, then post-exposure at regular intervals, and at the
conclusion of the remediation (Ferreira, 2011). Data are
available for 49 employees who recently concluded a two-year-
long lead remediation project. It is necessary to determine if
blood lead levels have increased.
1.2.6 Return-On-Investment
Sun Coast would like to know if all lines of service provide the

same return-on-investment. Return-on-investment data is
available for air monitoring, soil remediation, water
reclamation, and health and safety training. If return-on-
investment is not the same for all lines of service, it would be
helpful to know were differences exist (Ferreira, 2011). 1.3
Research Objectives
Sun Coast has identified several areas for concern that they
believe could be solved using business research methods. The
previous director was tasked with conducting research to help
provide information to make decisions about these issues.
Although data were collected, the project was never completed
(Creswell & Creswell, 2018). Senior leadership is interested in
seeing the project through to fruition. The organization seek to
achieve the objectives highlighted below in its endeavor to
promote effective leadership and efficiency of service provision
i) To determine the relationship between Particulate Matter
(PM) size and employee health.
ii) To evaluate if training has been successful in reducing lost-
time hours and, if so, how to predict lost-time hours from
training expenditures.
iii) To employ the historical data from 1,530 contracts to
predict the decibels (DB) levels of work environments before
placing employees on-site for future contracts,
iv) To evaluate whether the revised program is more effective
than the prior training program based on the two groups of
employees, A and B who participated in the prior training
program and in the revised training programs respectively
v) To determine if blood lead levels have increased based on 49
employees who recently concluded a 2-year lead remediation
project
vi) To evaluate where the difference between return on
investment exist considering four lines of service that Sun Coast
offers to their customers including air monitoring, soil
remediation, water reclamation, and health and safety
training.1.4 Research Questions and Hypotheses
Sun Coast faces different business problems which need to be

addressed as one means of promoting its endeavor to achieve
both short- and long-term goals. The research questions will
help the researchers is designing an appropriate research
process that will lead to a credible conclusion to the problem
(Creswell, & Creswell, 2018). On the hand, both null and
alternative hypotheses will enable the researchers to determine
the possible outcome based on the research problem.
i) Is there a relationship between Particulate Matter (PM) size
and employee health?
H0: PM that is less than 2.5 microns is potentially more
harmful than PM that is between 10 and 2.5
H1: There is no statistical relationship between the PM size
and the worker’s health.
ii) Has health and safety training successful in reduced lost-time
hours and, if so, how to can lost-time be predicted?
H0: There is no statistical relationship between health and
safety training and lost time.
H1: Health and safety training has successfully limited lost-
time hours and the lost time can be predicted based on current
performance
iii) Can (DB) levels of work environments be predicted before
placing employees on-site for future contracts?
H0: The standard earplugs are adequate to protect employee
hearing if the decibel levels are less than 120 decibels (dB).
H1: It is difficult to predict the DB levels since
there is no statistical link to it
iv) Which program is more effective between two groups of
employees, A and B prior training and during the revised
training respectively?
H0: The program is more efficient with the revised programs
than the prior training one.
H1: The program is more efficient with the one prior training
than revised one
v) Has the blood lead levels have increased based on 49

project.?
H0: The blood lead levels have increased considering the
sampled employees.
H1: The blood sugar of the employees was
maintained upon completion of a 2-year old lead remediation
project.
vi) Is there a difference between return on investment exist
considering four lines of service that Sun Coast offers to their
customers?
H0: There is a difference between the return on investment
H1: there is no statistical provision justifying the relationship
between the four lines of service that Sun Coast offers to their
customers
CHAPTER TWO2.1 Literature Review
This chapter covered a review of literature to the study. The
chapter covered the following: theoretical and empirical
literature leading to this study’s conceptual framework, accrual
theory of accounting, trade credit theory and agency theory. It
is after this evaluation that a literature gap is identified at the

end of this chapter with Sun Coast employee’s performance.2.2
Conceptual framework
CHAPTER THREERESEARCH METHODOLOGY, DESIGN,
AND METHODS3.1 Introduction
This chapter dealt with research design, location of the study,
target population, sample size, sampling procedures, research
instruments, instrument reliability, instrument validity, and data
collection procedures and data analysis techniques. A
discussion of each aspect of the research methodology was
given hereunder, beginning with research design3.2 Research
Design
The study used descriptive survey design combining both
qualitative and quantitative research strategies. The researcher
mainly employed descriptive correlation design, quantitative
approaches to get a clear knowledge and level of time losses,
employees health and training effect on Sun Coast employees.
Descriptive correlation design was an appropriate design
preferred by the researcher as it aim at establishing
relationships between dependent and independent variables
through quantifiable results (Wang et al., 2009). Descriptive
studies are regarded as non-experimental studies that are used to
describe characteristic of a certain individual or group under
study.it focuses on identifying association between variables
and formulating hypothesis upon making valid decisions and
conclusions after the findings.
3.3 Research Methods
Describe the research methods that will be used for this
research study. They may include survey, observation,
experimentation, descriptive, correlation, or causal comparative.
3.4 Data Collection Methods
The researcher used both primary and secondary data to

complete this research. Primary data was collected through
surveys and sampling techniques among Sun Coast employees
(Wang et al., 2009). Sampling is the act, process or technique of
selecting a suitable sample or representative part of a
population for the purpose of determining parameters or
characteristics of the whole population. A simple random
sampling was used in our survey where 103 job sites
employee,49 trained employees were used
.
Secondary data were obtained from different financial data
listed in 1,530 contracts information records on performance of
the employees.3.5 Sampling Design
Students should briefly describe the type of sampling design
that was most likely used for the data that were collected.
Choices include, but are not limited to, random sample,
convenience sample, etc. Explain your rationale for your
sampling design selection(s). 3.6 Questionnaires
The researcher developed the questioners to enable him to
collect data from the selected employees and employers of Sun
Coast. The aim of the questionnaires was to obtain the primary
data from the various employees on site in order to tap more
information from those who experienced health problems among
other employees. The respondents were instructed to answer all
questions in the questionnaire. The respondents were giving a
maximum of five days to understand the questionnaire and fill
all the fields. upon collection of the questionnaires from the
employee’s researcher and his assistant ensured that all
questionnaires had been answered3.7 Data Analysis Procedures
After the administration of the questionnaires the data gathered
was organized, collected, and encoded into the computer and
statistically treated using the MS-Excel. The researcher ran
descriptive statistics analysis based on frequency tables and
percentages based on the data collected (Creswell & Creswell,
2018). Both descriptive and inferential statistics were used in
the study where with respect to descriptive statistics, mean,
standard deviation and coefficient of variation were used to

establish core relationships between response variables in Sun
Coast management where null hypothesis for different research
questions was assessed against alternative hypothesis which
stated at least one was different. To ascertain the hypothesis the
researcher performed one-way and two- way ANOVA. The
researcher also performed F-tests to obtain critical values for
the test parameters. For inferential statistics the researcher
performed regression analysis where dependent variable were
Heath and ROI while (lost time, revised trainings, resources
such as air: water and soil, safety trainings e.t.c) were treated
as independent variables
3.8 Data Analysis
Details of how data is collected and analyzed is presented here.
The research that led to the achievement of Sun Coast
objectives was done using quantitative research methods since
they offer detailed insights pertaining to the study (Creswell &
Creswell, 2018). Research design is the specific type of study
that one would conduct and is usually consistent with one’s
philosophical worldview and the methodological approach the
researcher chooses
3.8.1 Correlation Analysis
microns
mean annual sick days per employee
microns
1
mean annual sick days per employee
-0.71598
1
From the above correlation analysis Pearson correlation
coefficient value between the test variables is negative value=-

0.71598 indicating that when one variable increases the effect
on other variable decreases (Wang et al., 2009). We accept
alternative hypothesis and conclude that There is no statistical
relationship between the PM size and the worker’s health.
3.8.2 Simple Regression Analysis
Model
Safety training=1753.602-6.157*lost time hours
From the above simple regression output since we have one
independent variable, we will pay attention to R square=0.8827
which shows high significance between the response variable
and predictor variable (Wang et al., 2009). We reject null
hypothesis and conclude that Health and safety training has
successfully limited lost-time hours and the lost time can be
predicted based on current performance
3.8.3 Multiple Regression Analysis
Model
Frequency (HZ) =32243.94-86.45*Angles in degrees-
741.55*chord length+42.06*velocity-65093.43*Displacemet-
241.1097*Decibel
Since it is a multi-regression analysis we focus on adjusted R-
squared=0.3385 which indicates low relationship between the
response and predictor variables taken as a pair (Wang et al.,

2009). we reject null hypothesis and conclude that the standard
earplugs are adequate to protect employee hearing if the decibel
levels are less than 120 decibels (dB).
3.8.4 Independent Samples t Test
From the above sample t test output, we can see that t-statistic
is=-9.666 <1.9876 critical t value hence we do not reject null
hypothesis. we conclude that It is difficult to predict the DB
levels since there is no statistical link (Wang et al., 2009).
3.8.5 Paired Samples t Test
From the output above t-statistic=1.9298>1.6777 critical value
hence we reject null hypothesis and conclude that the program
is more efficient with the revised programs than the prior
training one.
3.8.6 One-Way ANOVA
Anova: Single Factor

SUMMARY
Groups
Count
Sum
Average
Variance
A = Air
20
178
8.9
9.357895
B = Soil
20
182
9.1
3.042105
C = Water
20
140
7
6.631579

D = Training
20
108
5.4
1.410526
ANOVA
Source of Variation
SS
df
MS
F
P-value
F crit
Between Groups

182.8
3
60.93333333
11.9231
1.76E-06
2.724944
Within Groups
388.4
76
5.110526316
Total
571.2
79
ANOVA output above illustrate that P-value=0.00000176<0.05
hence it is statistically significant. F-value=11.9231>critical F-
value=2.72494 hence we reject null hypothesis and conclude
that There is a difference between the return on investment
CHAPTER FOURRESEARCH FINDINGS AND
DISCUSSIONS4.1 Introduction
This chapter presents analysis of the data on the effectof proper

health and safety measures on employees’ performance in Sun
coast company (Wang et al., 2009). The chapter also provides
the major findings and results of the study and discuss those
finding and results against employee’s health and work output.
This was mainly performed from data obtained from
questionnaires.4.2 Summary of key findings
The study found different findings based on different response
and predictor variables.
· Based on particulate matter size and employee’s heath with
regards to the sample dataset of 103 job sites it was evident that
is no statistical relationship between the PM size and the
worker’s health.
· Based on safety training expenditure our study showed that
Health and safety training has successfully limited lost-time
hours and the lost time can be predicted based on current
performance. The more training was conducted the lesser the
lost time experienced.
· Based on sound level exposure our research showed that
standard earplugs are adequate to protect employee hearing if
the decibel levels are less than 120 decibels (dB). For
environments with noise-levels exceeding 120 dB, more
advanced and expensive hearing protection is required, such as
earmuffs
· Based on employees training the study established that the
revised training program is more effective than the prior
training program.
· Based on lead exposure, our made us believe that It is
necessary to determine if blood lead levels have increased
through cross check monitoring.
· Based on ROI as an indicator for Sun coast profitability metric
we found that investment is not the same for all lines of service
i.e. soil, air and water.
CHAPTER FIVESUMMARY CONCLUSION AND

RECOMMENDATION5.0 Introduction
The chapter provides the summary of the findings from chapter
four, and it also gives the conclusion and recommendation of
the study based on the objectives of the study (Creswell &
Creswell, 2018). 5.1 Recommendations
The study recommended that
· Employees should not pay much attention to PM size in
refence to their heath rather they should focus on establish
proper measures to evade themselves from inhaling the particles
e.g. by wearing nose masks while on site for their health.
· More safety training should be exercised as their significantly
reduce lost time hours.
· Employees on noisy sites are recommended to have earplugs to
prevent ear damage and other hearing problems.
· Newly employed employees should be scheduled frequent
revised trainings in order to equip them with new and efficient
skills.
5.2 Conclusion
Based on the above findings employees on site should have
proper gears to prevent themselves from PM inhaling’s,
Frequent revised trainings on newly contracted employees were
necessary, Sun Coast management should mainly focus on the
utility which gains higher return on investment, Safety trainings
should be highly advocated to reduce lost times.
5.3 Suggestion for Further research
A similar research could be carried out among other industries
in the same category as sun Coast in order to find out whether
the same results will be obtained so as to allow generalization
of the results.
References
Ferreira, D. F. (2011). Sisvar: a computer statistical analysis

system. Ciência e agrotecnologia, 35(6), 1039-1042.
Wang, Y. Q., Zhang, X. Y., & Draxler, R. R. (2009). TrajStat:
GIS-based software that uses various trajectory statistical
analysis methods to identify potential sources from long-term
air pollution measurement data. Environmental Modelling and
Software, 24(8), 938-939.
Return-On-Investment
Particulate Matter (PM)
Lead Exposure
Safety Training Effectiveness
New Employee Training
SUMMARY OUTPUT
Regression Statistics
Multiple R0.939559324
R Square0.882771723
Adjusted R Square0.882241279
Standard Error161.302987
Observations223
ANOVA
dfSSMSFSignificance F
Regression143300521.43433005211664.2117.6586E-105
Residual2215750122.45126018.65
Total22249050643.88
CoefficientsStandard Errort StatP-valueLower 95%Upper
95%Lower 95.0%Upper 95.0%

Intercept1753.60213330.3629622357.754652.6E-
1351693.7641351813.4401321693.7641813.440132
lost time hours-6.1573943650.150935993-40.79477.7E-105-
6.45485242-5.85993631-6.45485-5.85993631
SUMMARY OUTPUT
Regression Statistics
Multiple R0.583706496
R Square0.340713274
Adjusted R Square0.338511248
Standard Error2564.049485
Observations1503
ANOVA
dfSSMSFSignificance F
Regression55.09E+091017230383154.72714711.2E-132
Residual14979.84E+096574349.763
Total15021.49E+10
CoefficientsStandard Errort StatP-valueLower 95%Upper
95%Lower 95.0%Upper 95.0%
Intercept32243.941721307.24124.665647385.2672E-
11329679.7234808.159929679.7235334808.1599
Angle in Degrees-86.4596200717.19892-5.0270363735.58105E-
07-120.196-52.72307055-120.1961696-52.72307055
Chord Length-741.55591911361.862-0.5445163360.586167308-
3412.921929.803715-3412.9155541929.803715
Velocity (Meters per
Second)42.060937514.2998949.7818547396.02337E-
2233.6264850.4953940833.6264809450.49539408
Displacement-65093.432458026.09-8.110229811.0415E-15-
80837-49349.85665-80837.00825-49349.85665
Decibel-241.109719210.26503-23.488460424.0652E-104-
261.245-220.974353-261.2450855-220.974353
t-Test: Two-Sample Assuming Unequal Variances
Group A Prior Training ScoresGroup B Revised Training Scores
Mean69.7903225884.77419355
Variance122.00449526.96456901
Observations6262

Hypothesized Mean Difference0
df87
t Stat-9.666557191
P(T<=t) one-tail9.69914E-16
t Critical one-tail1.662557349
P(T<=t) two-tail1.93983E-15
t Critical two-tail1.987608282
t-Test: Paired Two Sample for Means
Post-Exposure μg/dLPre-Exposure μg/dL
Mean33.2857142932.85714286
Variance155.5150.4583333
Observations4949
Pearson Correlation0.992236043
Hypothesized Mean Difference0
df48
t Stat1.929802563
P(T<=t) one-tail0.029776357
t Critical one-tail1.677224196
P(T<=t) two-tail0.059552714
t Critical two-tail2.010634758
Running Head: SUN COAST HEALTH
1
Sun Coast Health
By

Name
D
ate
Running Head: SUN COAST HEALTH 1
Sun Coast Health
By
Name
Date

Using t Test and ANOVA With Sun Coast Remediation Data Set
1
Using t Test and ANOVA With Sun Coast Remediation Data Set
5
Data Set
Tammie Witcher
Columbia Southern University
Data Analysis: Hypothesis Testing
Independent Samples t Test: Hypothesis Testing
Ho4: There is no statistically significant difference in mean
values for Group Aand Group B.
Ha4: There is a statistically significant difference in mean
values for Group Aand Group B.
Table 1: Two-sample independent t-test assuming equal
variances
Group A Prior Training Scores
Group B Revised Training Scores
Mean
69.79
84.77
Variance
122.00
26.96
Observations
62.00
62.00

Pooled Variance
74.48
Hypothesized Mean Difference
0.00
df
122.00
t Stat
-9.67
P(T<=t) one-tail
0.000
t Critical one-tail
1.66
P(T<=t) two-tail
0.000
t Critical two-tail
1.98
The two tailed p-value leads to the rejection of the null
hypothesis, t(122) = -9.67, p = 0.000. It is therefore concluded
that there is a statistically significant difference in the means
for group A (M = 69.79, SD = 122) and group B (M =84.77, SD
= 26.96) at the 1% level of significance.
Dependent Samples (Paired Samples) t Test: Hypothesis Testing
Ho5: There is no statistically significant difference in mean
values for Pre-Exposure μg/dLand Post-Exposure μg/dL.
Ha5: There is a statistically significant difference in mean
values for Pre-Exposure μg/dL and Post-Exposure μg/dL.
Table 2: Two-sample pared t-test

Pre-Exposure μg/dL
Post-Exposure μg/dL
Mean
32.86
33.29
Variance
150.46
155.50
Observations
49.00
49.00
Pearson Correlation
0.99
Hypothesized Mean Difference
0.00
df
48.00
t Stat
-1.93
P(T<=t) one-tail
0.030
t Critical one-tail
1.68
P(T<=t) two-tail
0.060
t Critical two-tail
2.01

The two tailed p-value of the paired t test leads to the rejection
of the null hypothesis, t(96) = -1.93, p = 0.030. It is therefore
concluded that there is a statistically significant difference in
the means for Pre-Exposure μg/dL (M = 32.86, SD = 150.46)
and Post-Exposure μg/dL (M = 33.29, SD = 155.50) at the 5%
level of significance.
ANOVA: Hypothesis Testing
Ho5: All the groups means are the equal.
Ha5: There exists at least one group mean that is statistically
significantly different from the other group means.
Table 3: Single factor analysis of variance
SUMMARY
Groups
Count
Sum
Average
Variance
A = Air
20.00
178.00
8.90
9.36
B = Soil
20.00
182.00
9.10

3.04
C = Water
20.00
140.00
7.00
6.63
D = Training
20.00
108.00
5.40
1.41
ANOVA

Source of Variation
SS
df
MS
F
P-value
F crit
Between Groups
182.80
3.00
60.93
11.92
0.000
2.72
Within Groups
388.40
76.00
5.11
Total
571.20
79.00

The p-value of the results of the ANOVA procedure leads to the
rejection of the null hypothesis that all the group means are
equal, F(3, 76) = 11.92, p = 0.000. It is therefore concluded that
there is at least one group mean that is statistically significantly
from the other means at the 1% level of significance.
Running head: RESEARCH OBJECTIVES 1
RESEARCH OBJECTIVES 5
Insert Title Here
Insert Your Name Here
Insert University Here
Research Objectives
Sun Coast has identified several areas for concern that they
believe could be solved using business research methods. The
previous director was tasked with conducting research to help
provide information to make decisions about these issues.
Although data were collected, the project was never completed.
Senior leadership is interested in seeing the project through to
fruition. The organization seek to achieve the objectives
highlighted below in its endeavor to promote effective
leadership and efficiency of service provision.
RO1: To determine the relationship between Particulate Matter

(PM) size and employee health.
RO2: To evaluate if training has been successful in reducing
lost-time hours and, if so, how to predict lost-time hours from
training expenditures.
RO3: To employ the historical data from 1,530 contracts to
predict the decibels (DB) levels of work environments before
placing employees on-site for future contracts.
RO4: To evaluate whether the revised program is more effective
than the prior training program based on the two groups of
employees, A and B who participated in the prior training
program and in the revised training programs respectively.
RO5: To determine if blood lead levels have increased based on
49 employees who recently concluded a 2-year lead remediation
project.
RO6: To evaluate where the difference between return on
investment exist considering four lines of service that Sun Coast
offers to their customers including air monitoring, soil
remediation, water reclamation, and health and safety training.
Research Questions and Hypotheses
Sun Coast faces different business problems which need to be
addressed as one means of promoting its endeavor to achieve
both short and long term goals. The research questions will help
the researchers is designing an appropriate research process that
will lead to a credible conclusion to the problem (Creswell, &
Creswell, 2018). On the hand, both null and alternative
hypotheses will enable the researchers to determine the possible
outcome based on the research problem.
RQ1: Is there a relationship between Particulate Matter (PM)
size and employee health?
H01: PM that is less than 2.5 microns is potentially more
harmful than PM that is between 10 and 2.5
HA1: There is no statistical relationship between the PM size
and the worker’s health
RQ2: Has health and safety training successful in reduced lost-

time hours and, if so, how to can lost-time be predicted?
H02: Health and safety training has successfully limited lost-
time hours and the lost time can be predicted based on current
performance.
HA2: There is no statistical relationship between health and
safety training and lost time. Hence, it is difficult to predict.
RQ3: Can (DB) levels of work environments be predicted before
placing employees on-site for future contracts?
H03: The standard ear-plugs are adequate to protect employee
hearing if the decibel levels are less than 120 decibels (dB).
HA3: It is difficult to predict the DB levels since there is no
statistical link to it.
RQ4: Which program is more effective between two groups of
employees, A and B prior training and during the revised
training respectively?
H04: The program is more efficient with the revised programs
than the prior training one.
HA4: The program is more efficient with the one prior training
than revised one.
RQ5: Has the blood lead levels have increased based on 49
project.
H05: The blood lead levels have increased considering the
sampled employees.
HA5: The blood sugar of the employees was maintained upon
completion of a 2-year old lead remediation project.
RQ6: Is there a difference between return on investment exist
considering four lines of service that Sun Coast offers to their
customers?
H06: There is a difference between the return on investment
HA6: there is no statistical provision justifying the relationship
between the four lines of service that Sun Coast offers to their
customers
References

Running Head: HUMAN RESOURCE 1
HUMAN RESOURCE 5
Human Resource
Name
Institution
Date
Human Resource
Central Tendency
In the measure of central tendency, the use of mean, mode or
median is common. Either measure depends on various
characteristic of the data. The use of mean is the most common
measure of central tendency. It is referred to us as average.
Mean is used when measuring tendency for both continuous and
discrete data. A person finds the sum of all variables in the data
and divides it to the number of values provided in the data set.
Using mean is important as it includes all variables in the data
set. Mean is better when sample size is large and when it does
not contain outliers.
The median measures the middle score for any data set arranged
using an order of magnitude. Notably, median is less affected

by skewed and outliers data. Instances when median is the best
measure of central tendency include when there are few extreme
scores in the data set distribution. Mean is affected by single
outliers but median does not. Second, when there are
undetermined or missing values in a data set, the median is
better. Third, when dealing with open ended distribution and the
data is on an ordinal scale, median gives best tendency measure
than mean.
The mode gives the most common variable in the data set. Mode
is commonly used when considering data in a nominal scale.
Mode is rarely used as it does not depict the best measure of
central tendency when a common variable is far away when
compared to the rest of the data set.
Multiple Regression
Multiple regression is more of an extended linear regression. It
measures the value or relationship between a dependent variable
(criterion or target variables) using two or more independent
variables (explanatory, regressor or predictor variables). In the
human resource domain, high employee performance is vital in
attaining increased overall return in an enterprise. Employee
performance is determined by other factors like fair
remuneration and incentives, training and development program,
supervision, good working environment and job security among
others. In determining the employees’ performance as the
dependent variable (target), the independent variable will be
fair remuneration and incentives, training and development
program, supervision, good working environment and job
security.
The assumption of the dependent and independent variables
satisfying the use of multiple regression include the dependent
variable having a continuous scale. The independent variables
must be either categorical (nominal or ordinal) or continuous.
The independent and dependent variables should have a linear
relationship. The data should show homoscedasticity where the
line of best fit remains similar. Moreover, the data must not
have multicollinearity to eliminate confusion on which

independent variable contributes variance in dependent variable.
Moreover, no significant outliers, high influential points or high
leverage points should occur when performing multiple
regression analysis. Lastly, errors must be normally distributed
to present the best result in the regression.
ANOVA
The analysis of variance (ANOVA) determines statistical
significant difference between means of independent groups.
There is two type of ANOVA namely, one-way ANOVA and
two-way ANOVA where there are one independent variable and
two independent variable in the test respectively. Taking an
example in a human resource field, the HR department wishes to
determine the level of occupational stress based on age. The
group gets classified into ages 35and below, 35-49 years and 50
years and above; groups are randomly split.
Using one-way ANOVA test in the example given above, a
person determines the mean score of occupational stress for
each group. A person set the confidence level in this instance at
95%. The p-value plays a major role of determining the
statistical significance. When the mean is less than or equal to
the p-value, a person rejects the null hypothesis and conclude
that the population mean are equal. On the other hand, if the p-
value is greater than the significance level set, a person accepts
the null hypothesis that not all population means are equal.
The basic condition while using ANOVA analysis are; the
dependent variable are normally distributed, homogeneity of
variance as well as independence of all observations made.
Risks
During a research, human participant may be exposed to
physical, economic, social, and psychological or health risk that
affects their after-life. Therefore, researchers must develop
some techniques to mitigate the risk associated with the study.
First, researchers should provide the consent form to the
participants. Participants in a research study make the decisions
that fit them when offered an accurate consent process. If the
participant accepts, it means the risks are manageable; there are

exemptions to the vulnerable and risk sensitive participants.
There should be excellent data collection and storage measures
to avoid loss of confidentiality which is a possible risk in
research study. Lastly, when the undesirable event happens,
researcher should have a contingency plan to alleviate the
victims from such risk. It includes having the right medical
practitioner always ready to treat the incidences or accidents
experienced.
However, sometimes the benefits of the research outweigh the
potential risks to human participants. Whenever the results of
the research develop a cure for the society mostly in medical
studies, the research study outweighs the risk. Similarly, in the
event that the research result guides the development of policies
used as reference to community wellbeing, and the harm is at
minimum as determined by the researcher, the research should
continue.
Application of Research Methods
Various aspects of research methods are applicable in life.
Ethics is a significant concept which assures that the
researchers gets the prerequisite authorization to conduct
research. The various ethic bodies ensure that human
participants are not exposed to harm, their confidentiality is
maintained, and the study is in line with moral standards of the
society and specific discipline. Seeking the sample population is
also important in life. Notably, a sample population should
represent the general population for the results to provide a
comprehensive knowledge/information about the population.
Therefore, there should be prudent choice of an appropriate
sampling design and data collection methods based on the
population under study
Given the two types of research studies qualitative and
quantitative determining which method to use gives a favorable
method to evaluate situation in the world. Qualitative method
undertakes the use of primary and measurable data while
quantitative evaluates secondary and non-measurable
information. Lastly, the statistical analysis of data is significant

to determine a conclusion of the research study. It is through
statistical or data analysis that research get inference to make
conclusion and propose future research studies.
Two issues I would like to study using research methods
include:
Which factors determines job satisfaction in employees?
How human resource hiring determines a company’s future
profitability index
Correlation Datajob sitemicronsmean annual sick days per
employee141126.573854855410677721185.5995710410114.581
28.5413771458159.531677179.55189.571951020662185227.562
38.55249425372667277.57287.55292.773029317.563294336634
3835853618378.55380.78390.58408.544128424.5943674477455
10462.5124759484749855057513.5952865355544.910557.5956
2.57576.575886595660766149628563186428654106649677768
5.2106957706671867267736.56741.58758.56762877857810479
1058088811882768310284788576866.57877.56888.568938900.
599118929693779429958.57963.5997389876995.571007.57101
0.2121024810359
Simple Regression Datacontract #safety training expenditurelost
time
hours11161985.12102051500.003021461126.464016031294.104
09141445.56404371112.47505981720.816028011789.66601420
1837.52606822000.00607142271.866023361507.977022161542.
337013271544.907010251547.16709201567.55704651600.0070
1381452.698014111500.008014391500.008023881326.6090184
91351.259017401380.699016661423.579016721095.581002455
1132.881004031134.581008561163.4410020501170.921001271
1177.0510024291188.191008121323.291004051051.171101630
1054.0311023761071.751108611077.8611024361080.34110369
23.3712030957.1712076964.111201155976.641201273982.0612
01886985.69120630985.971204071000.001204881002.2712019

711035.4312062910.85130118912.351302912.3513042917.0213
092917.02130160922.02130149922.02130178950.00130160804.
2814056826.86140152832.871403836.33140196842.961401578
56.99140221883.8814062888.5714064893.36140143900.891401
39910.85140217759.86150182767.13150162777.24150159784.2
2150162785.69150145785.69150203792.35150135799.9015015
8800.62150182801.5815035746.02160163756.98160105759.521
60163700.93170131709.4017073715.6817055724.94170181612.
23180124613.7018055624.54180136635.191805642.791801796
44.15180150656.14180140669.8118096669.81180157678.60180
51689.55180153526.48185137537.35185195561.96185116564.1
6185196566.5018565568.95185194574.51185113579.32185855
83.63185190601.43185148607.2618554611.48185183393.77190
100405.9119092406.81190157426.7819040429.50190217450.56
190137456.9019091471.8119096474.76190203500.9219020550
3.17190188507.6419029507.77190189509.4319034513.6919056
514.0719049526.14190145336.5120011344.3920075345.692001
55365.99200132377.93200168385.0520065391.7520038264.002
10186264.002109264.00210146265.40210123268.60210108275.
70210221283.16210201288.0021025288.0021072295.84210213
300.00210129305.27210195313.36210141244.0022075250.5722
033255.12220119256.89220205263.27220120263.60220110223.
64230208226.6523019230.00230211230.00230221231.5223023
233.29230133234.0023019234.0023084234.00230167234.00230
96234.00230200234.57230171236.6023018236.91230107237.70
230152243.24230120244.00230143218.0023524205.002401042
06.97240170215.37240115215.69240159215.99240125217.9924
0128218.00240105205.00245209169.58250190174.0925019917
9.06250110179.78250151192.61250219196.6525033201.362501
56202.71250161204.57250133205.00250131135.9926054145.59
26024152.6126048125.63270212128.0027091128.00270445.302
8018745.6928013450.002802451.23280656.8928013568.952801
32102.57280101102.5728034102.6428027103.5728086125.1828
0184731.362853570.9229014075.9029017397.97290131100.002
9042100.0029095100.0329017102.5629068102.5729019756.903
005956.90300268.0930011968.593001942.573202845.69320653

0.433301835.993309525.6934020126.603404026.9034019920.4
6360
Multiple Regression Datacontract #Frequency (Hz)Angle in
DegreesChord LengthVelocity (Meters per
Second)DisplacementDecibel142080000.180971.30.0026633712
6.201682100000.089371.30.00266337125.201714125000.03137
1.30.00266337125.9512336160000.186771.30.00266337127.591
2216200000.070571.30.00266337127.4611327250000.128171.3
0.00266337125.5711025315000.196671.30.00266337125.20192
0400000.135371.30.00266337123.061465500000.168871.30.002
66337121.301138630000.157871.30.00266337119.54114118000
00.055871.30.00266337117.15114391000000.151371.30.002663
37115.39123881250000.123871.30.00266337112.241184916000
00.127671.30.00266337108.721174050000.166655.50.00283081
126.416166663000.151555.50.00283081127.696167280000.122
955.50.00283081128.0862455100000.098555.50.00283081126.9
66403125000.172455.50.00283081126.086856160000.112755.5
0.00283081126.9862050200000.070555.50.00283081126.61612
71250000.198855.50.00283081124.1062429315000.115955.50.0
0283081123.236812400000.033255.50.00283081121.106405500
000.036455.50.00283081119.6061630630000.123155.50.002830
81117.9762376800000.065755.50.00283081116.4768611000000
.136555.50.00283081113.07624361250000.117855.50.00283081
111.0763620000.175239.60.00310138118.1293025000.188639.6
0.00310138119.3197631500.032839.60.00310138122.77911554
0000.04239.60.00310138124.809127350000.118839.60.0031013
8126.959188663000.140939.60.00310138128.62963080000.195
339.60.00310138129.099407100000.09539.60.00310138127.899
488125000.103339.60.00310138125.4991971160000.120539.60.
00310138124.04962200000.079239.60.00310138123.689118250
000.153839.60.00310138121.3992315000.112239.60.003101381
20.31942400000.111339.60.00310138119.22992500000.039539.
60.00310138117.789160630000.115839.60.00310138116.22914
9800000.153139.60.00310138114.7791781000000.053639.60.00
310138112.1391601250000.061439.60.00310138109.619562000
0.198931.70.00331266117.19515225000.14831.70.00331266118

.595331500.189431.70.00331266122.76519640000.100431.70.0
0331266125.04515750000.140231.70.00331266127.3152216300
0.091431.70.00331266129.0956280000.044331.70.00331266129
.23564100000.091731.70.00331266127.365143125000.073831.7
0.00331266124.355139160000.106931.70.00331266122.365217
200000.059231.70.00331266122.375182250000.130431.70.0033
1266120.755162315000.172231.70.00331266119.135159400000
.1431.70.00331266118.145162500000.053331.70.00331266115.
645145630000.038631.70.00331266113.775203800000.061131.
70.00331266110.5151351000000.051231.70.00331266108.2651
588001.50.052571.30.00336729127.12218210001.50.11971.30.0
0336729125.9923512501.50.050271.30.00336729125.87216316
001.50.167571.30.00336729126.63210520001.50.076971.30.003
36729126.64216325001.50.184871.30.00336729124.512131315
01.50.030171.30.00336729123.3927340001.50.108671.30.00336
729121.7625550001.50.033671.30.00336729119.63218163001.5
0.141471.30.00336729118.12212480001.50.090871.30.0033672
9115.37255100001.50.189971.30.00336729113.492136125001.5
0.041171.30.00336729109.2225160001.50.195271.30.00336729
106.5821793151.50.185439.60.00392107121.8511504001.50.15
9439.60.00392107124.0011405001.50.069439.60.00392107126.
661966301.50.11939.60.00392107128.3111578001.50.099239.6
0.00392107128.8315110001.50.161739.60.00392107127.581153
12501.50.035239.60.00392107125.21113716001.50.155839.60.0
0392107122.21119520001.50.192939.60.00392107122.1011162
5001.50.110639.60.00392107120.98119631501.50.037239.60.00
392107119.1116540001.50.078839.60.00392107117.741194500
01.50.144339.60.00392107116.24111363001.50.14539.60.00392
107114.7518580001.50.123539.60.00392107112.251190100001.
50.093939.60.00392107108.991148125001.50.130739.60.00392
107106.1115440030.136571.30.00425727127.56418350030.157
871.30.00425727128.45410063030.145371.30.00425727129.354
9280030.171171.30.00425727129.494157100030.104971.30.004
25727129.00440125030.120171.30.00425727127.634217160030
.106971.30.00425727126.514137200030.141571.30.0042572712
5.52491250030.137171.30.00425727124.02496315030.039671.3

0.00425727121.514203400030.180971.30.00425727120.264205
500030.135971.30.00425727118.134188630030.086271.30.0042
5727116.13429800030.171371.30.00425727114.6341891000030
.061771.30.00425727110.2243440030.035155.50.00452492126.
1595650030.086955.50.00452492128.1794963030.09255.50.004
52492129.56914580030.046355.50.00452492129.94911100030.
091255.50.00452492129.32975125030.118955.50.00452492127.
329155160030.072855.50.00452492124.439132200030.097455.
50.00452492123.069168250030.171955.50.00452492122.43965
315030.160155.50.00452492120.18938400030.136555.50.00452
492118.689186500030.176955.50.00452492117.3099630030.12
0755.50.00452492115.679146800030.046455.50.00452492113.7
991231000030.05455.50.00452492112.16910831530.132539.60.
00495741123.31222140030.134239.60.00495741125.472201500
30.075539.60.00495741127.6322563030.129939.60.0049574112
9.2927280030.093239.60.00495741129.552213100030.051439.6
0.00495741128.312129125030.108939.60.00495741125.802195
160030.088639.60.00495741122.782141200030.037539.60.0049
5741120.53275250030.136739.60.00495741120.16233315030.0
87839.60.00495741118.922119400030.052739.60.00495741116.
792205500030.080439.60.00495741115.792120630030.104639.
60.00495741114.042110800030.037639.60.00495741110.65220
831530.086631.70.00529514123.1181940030.170631.70.005295
14125.39821150030.041531.70.00529514127.54822163030.147
831.70.00529514128.6982380030.107631.70.00529514128.7081
33100030.126731.70.00529514126.83819125030.0531.70.00529
514124.83884160030.134731.70.00529514122.088167200030.1
08731.70.00529514120.08896250030.143531.70.00529514119.5
98200315030.197331.70.00529514118.108171400030.104431.7
0.00529514115.60818500030.084131.70.00529514113.8581076
30030.183831.70.00529514109.71815225040.09371.30.0049777
3126.39512031540.169771.30.00497773128.17514340040.0768
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813740012.30.096271.30.0337792139.4289150012.30.139371.3

Running Head: SUN COAST HEALTH 2Evaluating Health and Safety at Sun Coast: A Quantitative Analysis

Running Head: SUN COAST HEALTH 2Evaluating Health and Safety at Sun Coast: A Quantitative Analysis

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Running Head: SUN COAST HEALTH 2Evaluating Health and Safety at Sun Coast: A Quantitative Analysis