13Sun Coast Remediation ProjectMichell Mul

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Sun Coast Remediation Project
Michell Muldrow
Columbia Southern University
Research Methods
Dr. Senft
November 17, 2021
Table of Contents
Contents
Executive Summary 4
1.0 Introduction 5
1.1. Statement of Problems 5
1.1.1. Particulate Matter (PM) 5
1.1.2. Safety Training Effectiveness 6
1.1.3. Sound-Level Exposure 6
1.1.4. New Employee Training 6
1.1.5. Lead Exposure 7
1.1.6. Return-On-Investment 7
2.0. Literature Review 7
2.1. Particulate Matter (PM) Article 7
2.2. Safety Training Effectiveness 8
2.3. Sound-Level Exposure 9
2.4. New Employee Training 9
2.5. Lead Exposure 10
2.6. Return on Investment 10

3.0. Research Objectives, Research Questions, and Hypotheses
11
4.0. Research Methodology, Design, and Methods 14
4.1. Research Methodology 14
4.2. Research Design 14
4.3. Research Methods 15
4.3.1. Data Collection Methods 15
4.3.2. Sampling Design 15
5.0. Data Analysis Procedures 16
5.1. Data Analysis: Descriptive Statistics and Assumption
Testing 17
5.1.2.1. Frequency Distribution Table 20
Testing 30
6.0. Findings and Recommendation 42
6.1. Findings 42
6.2. Recommendations 43
6.2.1. Particulate Matter Recommendation 43
6.2.2. Safety Training Effectiveness Recommendation 43
6.2.3. Sound-Level Exposure Recommendation 43
6.2.4. New Employee Training Recommendation 44
6.2.5. Lead Exposure Recommendation. 44
6.2.6. Return on Investment Recommendation 44
References 45
Executive Summary
Business executives are primarily concerned about the strategies
to adopt to increase business transactions. Therefore, they
screen various aspects to determine the critical areas that
require to be solved using the business research method. The
senior leaders at Sun Coast want to see the projects conducted
to completion.
The paper comprises sections such as data collection, generating
statements of problems, literature review, research objectives,
research questions and hypothesis, methodology, design and

methods, data analysis, finding, and recommendation.
1.0 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
researching 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 following is the completion of
that project and includes a statement of the problems, literature
review, research objectives, research questions and hypotheses,
research methodology, design and methods, data analysis,
findings, and recommendations. 1.1. Statement of Problems
Six business problems were identified:
1.1.1. 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). Due to PM's smaller
size, less than 2.5 microns, is potentially more harmful than PM
between 10 and 2.5 since the conditions are more suitable for
inhalation. PM less than 2.5 can also 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. PM air
quality data have been collected from 103 job sites, which are
recorded in microns. Data are also available for average annual
sick days per employee per job site.
1.1.2. 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.
1.1.3. 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).
More advanced and expensive hearing protection is required for
environments with noise levels exceeding 120 dB, such as
earmuffs. Historical data have been collected from 1,503
contracts for several variables that are believed to contribute to
excessive dB levels. It would be important if these data could
be used to predict the dB levels of work environments before
placing employees on-site for future contracts. This would help
the safety department plan for the procurement of appropriate
ear protection for employees.
1.1.4. 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. Upon completion of the training
programs, the employees are tested on their knowledge. Test
data are available for two Groups; a) Group A employees who
participated in the prior 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.
1.1.5. Lead Exposure
Employees working on job sites to remediate lead must be
monitored. Lead levels in the blood are measured as micrograms

of lead per deciliter of blood (μg/dL). A baseline blood test is
taken pre-exposure and post-exposure at the conclusion of the
remediation. 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.
1.1.6. Return-On-Investment
Sun Coast offers four service lines to their customers, including
air monitoring, soil remediation, water reclamation, and health
and safety training. Sun Coast would like to know if each line
of service offers the same return-on-investment. Return-on-
investment data are available for air monitoring, soil
remediation, water reclamation, and health and safety training
projects. If return-on-investment is not the same for all service
lines, it would be helpful to know where differences exist. 2.0.
Literature Review2.1. Particulate Matter (PM) Article
Vinnikov, D., Tulekov, Z., & (Raushanova, A. (2020).
Occupational exposure to participate matter from air pollution
in the outdoor workplaces in Almaty during the cold season.
PloS one, 15(1).
The article authors are Al-Farabi Kazakh National
University, School of Public Health, Almaty, Kazakhstan,
National Research Tomsk State University; hence they qualify
to write it. Vinnikov et al. (2020), the primary purpose was to
study the occupational particulate matter's level in outdoor work
settings during the cold season. The study used AVOVA in data
analysis. Despite the research in Almaty, the same urban
landscape gives a similar concept regarding the association
between increases of particulate matter in the cold season. The
researchers established that M10 TWA lay between 0.050 to
2.075 mg/m3 with 0.366 as geometric mean and median 0.352
mg/m3, implying a high level of particulate matter. I believe
that the research will help implement ways to prevent pollutants
at work based on the research's evidence-based findings.2.2.
Safety Training Effectiveness
Hill III, J.J., Slade, M.D., Cantley, L., Vegso, S., Fiellin, M., &

Cullen, M.R. (2008). The relationships between lost work time
and duration of absence spell a proposal for a payroll-driven
absenteeism measure. Journal of occupational and
environmental medicine/American College of Occupation and
Environmental Medicine, 50(7), 840.
The above article, its authors are affiliates of recognized
institutions of higher learning such as Yale University. The
study's purpose was to establish critical metrics for use in
determining the lost work time and duration of absences in work
resulting from training. The research utilized ANOVA in
determining the relationship between the work lost rate and
expenditures within a healthcare context. The findings showed
that hours not paid and absent days are significantly correlated
with the work loss rate. The research and Sun Coast aim at
establishing whether safety training can help reduce
absenteeism resulting from workplace injuries. The research
made a positive organizational impact in organizations can rely
on workforce databases to study the absenteeism patterns and
the leading cause and if these causing factors get attributed to
lack of training. 2.3. Sound-Level Exposure
Byers, J., Waugh, W. R., & Lowman, L. (2006). Sound level
exposure of high-risk infants in different environmental
conditions. Neonatal Network, 25(1), 25-
32.https://connect.springerpub.com/content/sgrnn/25/1/25.abstra
ct
The above article authors have acquired a masters' degree
and above from recognized universities. The research employed
a descriptive and comparative approach, and it used a
convenience sample of 134 babies. It was established that
respiratory therapy equipment, employee talking, alerts, and
infant fussiness lead to high sound levels. Also, the findings
showed that 4–6 dB is an effective sound level reduction
compared to noise levels that exceed 120 dB, as portrayed by
Dun Coast. The latter can protect workers' ears. Thus both the
research and Sun Coast want to establish the impact of high-
level sound on ears. Through this research, Sun Coast's safety

department has a positive organizational impact to rely on the
evidence-based sound-reducing strategies that the study
proposes.2.4. New Employee Training
Sharma, R., & Mishra, D. K. (2020). The role of safety training
in original equipment manufacturing companies impacts
employee perception of knowledge, behavior towards safety,
and a safe work environment. International Journal of Safety
and Security Engineering, 10(5), 689-
698.file:///C:/Users/user/Downloads/10.05_14.pdf
The article authors are affiliates of Deemed University.
The purpose of their study was toresearch the impact of safety
training on employees' practices or behaviors on safety and a
safe working environment. The study employed a survey
research design whereby 23 respondents participated in a pilot
survey. The researchers used a Cronbach alpha (α) to determine
the consistency of the questionnaire and SPSS vs. 21.0 (IBM) to
analyze the collected data. The results are that safety training
does not help in changing safety behaviors. Both the article and
Sun Coast aimed at finding whether safety training helps change
employees' safety behavior at the workplace (self- behavioral
change towards safety issues). This research will help Sun
Coast explore other ways of enhancing safety since safety
training seems ineffective based on the research findings.2.5.
Lead Exposure
Guth, K., Bourgeois, M., Johnson, G., & Harbison, R. (2020).
Assessment of lead exposure controls on bridge painting
projects using worker blood lead levels. Regulatory Toxicology
and Pharmacology, 115, 104698
https://www.sciencedirect.com/science/article/abs/pii/S0273230
020301240
All the authors are experts in occupational health and
safety and affiliates of the University of South Florida. The
main purpose of the research was to study the exposure profile
and compare it with the OSHA's construction lead standards.
The used method was comparative or quasi-Experimental to
help in establishing cause-effect relationships among various

exposures to lead. The findings revealed that laborers' and
painters' exposure to lead is greater than the set OSHA
construction lead standards. Both the research and Sun Coast
aim at establishing the risks associated with the workers' level
of lead exposure. Thus, I believe this research will help Sun
Coast differentiate between effective and ineffective lead
exposure controls or methods to ensure the safety of
workers.2.6. Return on Investment
Hutauruk, M. R., & Ghozali, I. (2020). Overview of return on
investment on cigarette companies registered in Indonesia stock
exchange. International Journal of Scientific and Technology
Research.
Authors are affiliates of recognized universities such as
the University of New York and the University of Liverpool.
The research purpose was to justify the effect of investment
returns in profitability on stock prices. The researchers used
online data in IDX for data collection. The findings showed that
return on equity ROE has a positive and crucial impact on stock
prices. The general results were that return on equity, asset, and
earning per share significantly affect stock price movements.
The existing relationship between the article and Sun Coast is
that the two aim at determining the viability of the projects to
invest in. I believe that this research will help Sun Coast to rely
on ROA, ROE, and EPS as the best investment performance
measurement techniques for determining the key behaviors of
market players.3.0. Research Objectives, Research Questions,
and Hypotheses
The first project's objective is to determine the variation
of respiratory complications during pre-exposure and post-
exposure at the end of the remediation program. This objective
helps understand the exposure that presents more respiratory
risks than the other. The second aim is to establish if
employees' absenteeism is attributed to injuries resulting from
inadequate training. This second objective explores how
insufficient or ineffective training increases injury rates of
incidents, which contributes to workforce absenteeism (Gianino

et al., 2019). The third objective is to establish whether
standard earplugs are adequate to protect employees' ears if the
decibel levels are less than 120 decibels. It helps in knowing the
standard decibels for maintaining a healthy eardrum at the
workplace.
The fourth objective is to establish whether the new
training program is more effective than the earlier training
intervention. It enhances the comparison between the two pieces
of training to select the best one to implement in improving
health and safety at the workplace. The fifth objective is to
explore the variation of respiratory complications during pre-
exposure and post-exposure at the end of the remediation
program. Through this objective, the organization knows the
exposure leads to more severe complications than the other. The
final aim is to establish the existing differences in return on
investment for all lines of service. It helps determine the
current gap of return on investment to make a good investment
decision.
The other objective is to investigate the levels of
respiratory complications before and after remediation program
exposure; this will help identify the impact of the remediation
program on employees' respiratory complications incidences.
The last goal is establishing the effect of lost-time hours on the
general organizational performance. This goal with help
understand how lost time hours through sick leaves affect the
organization's revenue and profits.
Good research questions and hypotheses are developed
from identifying gaps and developing new ideas to fill the gaps
(Cai et al., 2019). Additionally, research questions must build
on the existing literature by recognizing its assumptions.
Research questions progress from the known facts to the
unknown statement that requires validation (Francis et al.,
2017). Similarly, the presented research questions and
hypotheses evaluate facts and the unknown factors to establish
solutions.
RO1: Determine if there is a relationship between PM size and

employee health.
RQ1: Is there a relationship between particulate matter size and
employee sick days?
Ho1: There is no statistically significant connection between
particulate matter size and employee health.
Ha1: The alternative hypothesis is the direct opposite of the null
hypothesis.
RO2: Predict lost-time hours from training expenditures
RQ2: Is there a relationship between safety training expenditure
and lost-time hours?
Ho2: There is no statistically significant relationship between
safety training expenditure and lost-time hours.
hypothesis.
RO3: Predict the dB level of work environments.
RQ3: Is there a relationship between frequency, angle in
degrees, chord length, velocity, and displacement, and decibel
level?
frequency, angle in degrees, chord length, velocity, and
displacement, and decibel level.
hypothesis.
RO4: Determine if the revised training program is more
RQ4: Is the revised new employee training program more
effective than the prior training program?
Ho4: There is no statistically significant difference in mean
scores between prior training and revised training.
Ha4: There are statistical differences in the effectiveness of
training for employees' groups.
RO5: Determine if employee blood lead levels have increased.

RQ5: Have employee blood lead levels increased from their pre -
exposure baseline measurements?
Ho5: There is no statistically significant difference in employee
blood lead levels between pre-exposure and post-exposure.
hypothesis.
RO6: Determine if the return on investment is the same for all
Sun Coastlines of service.
RQ6: Are there differences in return on investment between air
monitoring, soil remediation, water reclamation, and health and
safety training?
Ho6: There are no statistically significant differences in ROI
between air monitoring, soil remediation, water reclamation,
and health and safety training.
hypothesis.4.0. Research Methodology, Design, and
Methods4.1. Research Methodology
The selected research methodology is quantitative. Using this
methodology, a researcher can use numbers and graphs to
express the collected data when confirming the theories and
assumptions about the research problem. Therefore, the
procedure enables an in-depth understanding of the relationship
between an independent and dependent variable in a population.
In sum, the primary reasons for selecting quantitative over
qualitative methods are that it is more scientific, objective, and
control-sensitive.4.2. Research Design
For this project, the research design should be descriptive (non-
experimental). This design will give the best results when
testing the research hypothesis on the six identified problems.
The design is helpful when describing a relationship between
two or more variables, all without any interference from the
researcher. For instance, in Sun Coast, the issue of employee
safety has inadequate training as the causing factor for
workplace injuries (effect). Therefore, this researcher will
examine the relationship between training and injuries

witnessed among the employees. These aspects will make the
critical variables for establishing the connection.4.3. Research
Methods
The research methods that will be used for this project will be
descriptive, correlational, and causal-comparative.
Descriptive research often involves collecting information
through data review, surveys, interviews, or observation.
Correlational Research is used to test a null hypothesis stating
no relationship exists between variables.
Causal-comparative research attempts to identify a cause-effect
relationship between two or more groups.
4.3.1. Data Collection Methods
The data collection methods that will be used is a survey in
which contact can be made via telephone, which can include a
skype call or video conference, mail-in which a questionnaire
can be sent out, electronically where a survey can be sent
through email, observation in which a researcher can count the
number of people attending a certain event and finally document
analysis which uses public records to gather information.
4.3.2. Sampling Design
Sampling design is part of the research methodology, and it
considers the total number of Sun Coast employees as the target
finite population. Therefore, a sample will represent the whole
workforce population in which the people to make the sample
will be randomly selected. This random selection implies that
the researcher will give each employee an equal probability of
being chosen. Thus, a random sample becomes the sampling
design for this study. 5.0. Data Analysis Procedures
P1- This problem will use a correlation analysis to determine
the existing association between the two variables by computing
their relationship. A high correlation will imply a cause-effect
relationship through this approach, while a low correlation will
mean a weak connection between the variables (Tabuena &
Hilario, 2021).

P2- This problem will use simple regression to determine
critical factors and the ones not crucial to ignore.
P3- This problem will use multiple regression to determine if
additional research is needed or when multiple X variables are
included in the analysis to make a prediction about a change in
a single Y variable.
P4- This problem will use the independent sample t-test, a null
hypothesis stating there is no statistically significant difference
between the two means.
P5- This problem will use the dependent sample t-test to
determine whether the mean difference between two sets of
observations is zero.
P6- This problem will use an ANOVA test that is like the t-test;
however, it will determine if a null hypothesis that no
statistically significant differences exist among means for three
or more groups.
5.1. Data Analysis: Descriptive Statistics and Assumption
Testing
The main assumptions of a parametric test include normality of
the distribution, where the histogram should show asymmetric
bell shape. The other assumption is the homogeneity of variance
and the linearity of the data.
5.1.1. Correlation: Descriptive Statistics and Assumption
Testing5.1.1.1. Frequency Distribution Table
Histogram
Bin
Frequency

More
2
From the figure above, the histogram obtained is a bell-shaped
histogram. This shows that the data is normally distributed with
a mean of approximately 7. This implies that the assumption of
normality is met since the data is symmetric. However, the
figure shows that more data in the datasets are skewed to the
right than those to the left.
Descriptive Statistics Table
mean annual sick days per employee
Mean
7.126213592
Standard Error
0.186483898
Median
7
Mode
7
Standard Deviation
1.892604864
Sample Variance
3.58195317
Kurtosis
0.124922603
Skewness

0.142249784
Range
10
Minimum
2
Maximum
12
Sum
734
Count
103Measurement Scale
The measurement scale used in the data is ratio scale. This is
because the dataset values cannot take a negative value since
the smallest value is 2.Measure of Central Tendency
The Mean Sun Coast Remediation for this data is 7.1262139,
with a median of 7 and a mode of 7. The range between the
maximum and the maximum value for this data is 10, with the
maximum value being 12 and the minimum value being
2.Skewness and Kurtosis
The skewness value for this data is 0.1422, and Similarly, the
kurtosis value is 0.124922603. This, therefore, implies that the
data is slightly skewed to the right. However, the amount of
skewness in the data is minimal since the skewness and kurtosis
values are both less than 0.5.
Evaluation
From the above histogram, the symmetrical shape of the
histogram shows that the assumption of normality is met.
Similarly, the linearity of data and the homogeneity of variance
assumptions are met by the data and the analysis results
provided.
5.1.2. Simple Regression: Descriptive Statistics and Assumption
Testing
5.1.2.1. Frequency Distribution Table
Histogram

More
4
a mean of approximately 200. This implies that the assumption
of normality is met since the data is symmetric. However, the
figure shows that more data in the datasets lies to the right than
those to the left.Descriptive Statistics Table
lost time hours
Mean
188.0044843
Standard Error
4.803089447
Median
190
Mode
190

Standard Deviation
71.72542099
Sample Variance
5144.536016
Kurtosis
-0.50122353
Skewness
-0.08198487
Range
350
Minimum
10
Maximum
360
Sum
41925
Count
223Measurement Scale
the smallest value is 10.Measure of Central Tendency
The mean value for this data is 188.0044843, with a median of
190 and a mode of 190. The range between the maximum and
the maximum value for this data is 350, with the maximum
value being 360 and the minimum value being 10.
Skewness and Kurtosis
The skewness value for this data is -0.08198487, and Similarly,
the kurtosis value is -0.50122353. This, therefore, implies that
the data is slightly skewed to the left since the skew ness and
kurtosis values both have negative signs. However, the amount
of skewness in the data is very little since the skewness and
kurtosis values are both between -0.5 and 0.5.Evaluation

provided.
5.1.3. Multiple Regressions: Descriptive Statistics and
Assumption Testing
Histogram
Bin
Frequency
103.38
1
104.3697
2

105.3593
1
106.349
3
107.3386
6

108.3283
6
109.3179
9
110.3076
12
111.2973

18
112.2869
17
113.2766
26
114.2662
22

115.2559
27
116.2456
47
117.2352
36

118.2249
44
119.2145
47
120.2042
53

121.1938
61
122.1835
60
123.1732
62
124.1628
74

125.1525
70
126.1421
81
127.1318
93

128.1214
73
129.1111
105
130.1008
80

131.0904
88
132.0801
67
133.0697
50

134.0594
56
135.0491
35
136.0387
30
137.0284
19

138.018
7
139.0077
8
139.9973
5

More
2
From the figure above, the histogram obtained is not bell -
shaped. This shows that the data is not normally distributed
with a mean of approximately 130. This implies that the
assumption of normality is not met since the data is skewed to
the left.Descriptive Statistics Table
Decibel
Mean
124.8359
Standard Error
0.177945
Median
125.721
Mode
127.315
Standard Deviation
6.898657
Sample Variance
47.59146

Kurtosis
-0.31419
Skewness
-0.41895
Range
37.607
Minimum
103.38
Maximum
140.987
Sum
187628.4
Count
1503
Measurement Scale
the smallest value is 103.38Measure of Central Tendency
125.721 and a mode of 127.315. The range between the
maximum and the maximum value for this data is 37.607, with
the maximum value being 140.987and the minimum value being
103.38.Skewness and Kurtosis
The skewness value for this data is -0.41895. Similarly, the
kurtosis value is -0.31419. This, therefore, implies that the data
is slightly skewed to the left since the skewness and kurtosis
values both have negative signs. However, the amount of
skewness in the data is very little since the skewness and
kurtosis values are both between -0.5 and 0.5.Evaluation
5.1.4. Independent Samples t-Test: Descriptive Statistics and
Assumption Testing
Histogram
Bin

Frequency
50
4
55.85714
5
61.71429
7

67.57143
8
73.42857
14
79.28571
10
85.14286

From the figure above, the histogra m obtained is a bell-shaped
normality is met since the data is symmetric. However, the
figure shows that more data in the datasets lies to the left than
those to the right.
Frequency
2
5

normality is met since the data is symmetric.Descriptive
Statistics Table
Group A Prior Training Scores
Mean
69.79032258
Standard Error
1.402788093
Median
70
Mode
80
Standard Deviation
11.04556449
Sample Variance
122.004495
Kurtosis
-0.77667598
Skewness
-0.086798138
Range

41
Minimum
50
Maximum
91
Sum
4327
Count
62
Group B Revised Training Scores
Mean
84.77419355
Standard Error
0.659478888
Median
85
Mode
85
Standard Deviation
5.192741955
Sample Variance
26.96456901
Kurtosis
-0.352537913
Skewness
0.144084526
Range
22
Minimum
75
Maximum
97
Sum

5256
Count
62
Measurement Scale
For both diagrams, the measurement scale used in the data is
ratio scale. This is because the dataset values cannot take a
negative value since the smallest value are 50 and 75.Measure
of Central Tendency
For the first diagram, the mean value for this data is
84.77419355, with a median of 85 and a mode of 85. The range
between the maximum and the maximum value for this data is
22, with the maximum value being 97 and the minimum value
being 75.
For the second diagram, the mean value for this data is
being 50.
The measures of central tendencies are, therefore, all relevant to
the data.Skewness and Kurtosis
kurtosis value is -0.31419. Therefore, this implies that the data
values have negative signs. However, the amount of skewness in
the data is very little since the skewness and kurtosis values are
both between -0.5 and 0.5.Evaluation
being 75.
being 50.

the data.
The parametric test assumptions of linearity and normality were
met in the data.
5.1.5. Dependent Samples (Paired-Samples) t-Test: Descriptive
Statistics and AssumptionTestingFrequency Distribution Table
Histogram
Bin
Frequency
6
1
13.14286
3

20.28571
5
27.42857
6
34.57143
8

From the figure above, the histogram obtained is skewed to the
left. These figures, therefore, show that the data is not normally
distributed with a mean of approximately 34. This implies that
the assumption of normality is met since the data is not
symmetric.
Bin
Frequency
6
1

13.14286
3
20.28571
5
27.42857
6
34.57143

8
41.71429
11
48.85714
11
More
4

left. This figure, therefore, shows that the data is not normally
the assumption of normality is met since the data is not
symmetric.
Pre-Exposure μg/dL
Mean
32.85714286
Standard Error
1.752306546

Median
35
Mode
36
Standard Deviation
12.26614582
Sample Variance
150.4583333
Kurtosis
-0.576037127
Skewness
-0.425109654
Range
50
Minimum
6
Maximum
56
Sum
1610
Count
49
Post-Exposure μg/dL
Mean
33.28571429
Standard Error
1.781423416
Median
36
Mode
38
Standard Deviation
12.46996391

Sample Variance
155.5
Kurtosis
-0.654212507
Skewness
-0.483629097
Range
50
Minimum
6
Maximum
56
Sum
1631
Count
49Measurement Scale
negative value since the smallest values are both 6.Measure of
Central Tendency
being 6.
being 6.
the data.
kurtosis value is -0.654212507. This, therefore, implies that the
data is slightly skewed to the left since the skewness and

kurtosis values both have negative signs. However, skewness in
the data is significant since the skewness and kurtosis values
are less than -0.5.Evaluation
From the above diagrams, the skewness and kurtosis values are
negative. Similarly, the above histogram figures clearly show
that the datasets are skewed to the left; thus, the assumption of
normality is not met. Besides, since the normality is not met, we
conclude that the homogeneity assumptions have not been met
either.
5.1.6. ANOVA: Descriptive Statistics and Assumption Testing
Histogram
Bin
Frequency
3
1

left. This figures, therefore, show that the data is not normally
the assumption of normality is not met since the data is not
symmetric.
Bin
Frequency

From the figure above, the histogram obtained is approximately
average. This figure, therefore, shows that the data is not
normally distributed with a mean of approximately 8. This

implies that the assumption of normality is met since the data is
approximately symmetric.
Bin
Frequency
3
1
4.25
3
5.5
7

average. This figure, therefore, shows that the data is not
normally distributed with a mean of approximately 6. This
implies that the assumption of normality is met since the data is
approximately symmetric.Descriptive Statistics
TableMeasurement Scale
The measurement scale used in the data obtained is the is
nominal scale. This is because the data variables such as water,
soil, and training cannot be categorized according to the order
but rather are random labels whose ordering has no meaning.
8.9
0.684028
9
11
3.059068
9.357895
-0.6283
-0.36085
11
3
14
178
20
For the tables above, the mean value for this data variable is
8.9, with a median of 9 and 11. The range between the

total value being 14 and the minimum value being 3.
B = Soil
Mean
9.1
Standard Error
0.390007
Median
9
Mode
8
Standard Deviation
1.744163
Sample Variance
3.042105
Kurtosis
0.11923
Skewness
0.492002
Range
7
Minimum
6
Maximum
13
Sum
182
Count
20
For the tables above, mean value for this data for the variable
soil is 9.1 with a median of 9 and mode of 8. The range between
the maximum and the maximum value for this data is 7, with the
full value being 13 and the minimum value being 6.

C = Water
Mean
7
Standard Error
0.575829
Median
6
Mode
6
Standard Deviation
2.575185
Sample Variance
6.631579
Kurtosis
-0.23752
Skewness
0.760206
Range
9
Minimum
3
Maximum
12
Sum
140
Count
20
For the tables above, the mean value for the variable water is 7
maximum value being 12 and the minimum value being 3.
D = Training

Mean
5.4
Standard Error
0.265568
Median
5
Mode
5
Standard Deviation
1.187656
Sample Variance
1.410526
Kurtosis
0.253747
Skewness
0.159183
Range
5
Minimum
3
Maximum
8
Sum
108
Count
20
For the tables above, the mean value for variable training is 5.4
total value being eight and the minimum value being 3.Measure
of Central TendencySkewness and Kurtosis
The skewness value for variable 1 is -0.36085. Similarly, the
kurtosis value is -0.6283. This, therefore, implies that the data

skewness in the data is small since the skewness value is more
significant than -0.5.
The skewness value for the variable soil is 0.492002. Similarly,
the kurtosis value is 0.11923. Therefore, this implies that the
data is slightly skewed to the right since the skewness and
kurtosis values have positive signs. However, the amount of
skewness in the data is negligible since the skewness value is
less than 0.5.
The skewness value for the variable soil is 0.76020. Therefore,
it implies that the data is skewed to the right since the skewness
has a positive sign. However, the amount of skewness in the
data is significant since the skewness value is greater than 0.5.
The skewness value for the variable training is 0.159183.
Therefore, it implies that the data is skewed to the right since
the skewness has a positive sign. However, the amount of
skewness in the data is small since the skewness value is less
than 0.5.
Evaluation
The parametric test assumption for homogeneity and normality
is not met since data values are skewed to the right while some
are skewed to the left. The linearity assumption is not satisfied
either. 6.0. Findings and Recommendation6.1. Findings
RO1: Determine how PM affects employee's heath at Sun Coast
The results of the statistical Testing showed that a person's
PM is related to their employee health. It is a relatively strong
and positive relationship between Particulate matter and health.
We would, therefore, expect to see in our population high levels
of particulate matter people having a greater risk of poor health.
RO2: We should determine if safety training was indeed
practical for staff
The statistical Testing showed that safety training was
indeed practical for the team. The employees should be trained
to reduce any work-related injuries and safety precautions in the
workplace.
RO3: Determine if Sun Coast received a return of investment

for the services offered to customers
The statistical Testing illustrates that the Sun Coast had a
significant mean from the other groups on investment; hence the
firms received a return on investment.
RO4: We should next determine how much lead exposure
employees are contaminated with lead
The statistics testing showed low levels of lead exposure to the
staff. Although there are no recommended levels of zinc
exposure, the low levels illustrate that the organization has
achieved it.
RO5: Determine how sound level exposure affects employees'
hearing.
The sound level exposure may affect the employee's hearing and
hence impact productivity. Organizations need to control the
employee exposure to the sound levels. If they cannot control
noise from outside, they need to provide employees with
hearing devices to limit the excess noise pollution.
RO6: Determine how practical new hire training is working
The statistics significantly illustrate that new hire training is
based on how the employees effectively settle within the
organizations and carry out their daily activities. 6.2.
Recommendations
6.2.1. Particulate Matter Recommendation
The US exposure rates to delicate matter such as fine PM2 can
be considered safe via the US environmental protection agency's
national ambient air quality standards. However, individuals
have to breathe a limit of up to 12 micrograms per cubic meter
of air (ug/m3).
6.2.2. Safety Training Effectiveness Recommendation
It is essential to carry out safety training since the employees
must have technical knowledge on handling equipment in the
workplace and avoid injuries.
6.2.3. Sound-Level Exposure Recommendation

There recommended NIOSH exposure limit for occupational
notices is 85 decibels. It is recommended to utilize hearing
protection in the event the hazardous noise levels are not
adequately reduced.
6.2.4. New Employee Training Recommendation
A business must train the new staff on proper safety and PPE
use, including protective equipment like earplugs, safety
goggles, lockout ladders, safely wipe up any spills, and other
helpful training techniques to reduce the instance of injuries.
6.2.5. Lead Exposure Recommendation.
It is crucial to understand that there is no safe blood level of
lead, but a five mcg/dl can be used to illustrate unsafe levels for
children, and hence the blood levels need to be tested
periodically.
6.2.6. Return on an Investment recommendation
Investors must expect some realistic return for their investment,
and a good return on investment is considered about 7% per
annum.
References

conditions. Neonatal Network, 25(1), 25-32.
https://connect.springerpub.com/content/sgrnn/25/1/25.abstract
Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V.,
Cirillo, M., ... & Hiebert, J. (2019). Posing significant research
questions. Journal for Research in Mathematics Education,
50(2), 114-120.
Creswell, J. W., & Creswell, J. D. (2018). Research design:
Qualitative, quantitative, and mixed methods approaches (5th
ed.). SAGE.
Gianino, M. M., Politano, G., Scarmozzino, A., Stillo, M.,
Amprino, V., Di Carlo, S., ... & Zotti, C. M. (2019). Cost of
sickness absenteeism during seasonal influenza outbreaks of
medium intensity among health care workers. International
journal of environmental research and public health, 16(5), 747.
and duration of absence spells proposal for a payroll driven
measure of absenteeism. Journal of occupational and
Environmental Medicine, 50(7),
840.https://www.youtube.com/watch?v=kr64tfZmiGA
Research.
Porterfield, T. (2017, May 18). Excel 2016 Correlation Analysis
[Video file]. Retrieved from manufacturing companies on
employee perception of knowledge, behavior towards safety and
safe work environment.
Seber, G. A., & Lee, A. J. (2012). Linear regression analysis
(Vol. 329). John Wiley & Sons.

in original equipment International Journal of Safety and
Security Engineering, 10(5), 689-698.
file:///C:/Users/user/Downloads/10.05_14.pdf
Occupational exposure to participate matter from air pollution
PloS one, 15(1).
Histogram
Frequency 2 3 4 5 6 7 8 9 10 11
More 1 1 5 13 18 24 18 12 7 2
2
Bin
Frequency
Histogram
Frequency 10 35 60 85 110 135 160 185 210
235 260 285 310 335 More 1 1 9 9 17
18 24 27 37 24 21 15 12 4 4
Bin
Frequency
Histogram
Frequency 103.38 104.3696579 105.3593158
106.3489737 107.3386316 108.3282895 109.3179474
110.3076053 111.2972632 112.2869211 113.2765789
114.2662368 115.2558947 116.2455526 117.2352105
118.2248684 119.2145263 120.2041842 121.1938421
122.1835 12 3.1731579 124.1628158 125.1524737
126.1421316 127.1317895 128.1214474 129.1111053
130.1007632 131.0904211 132.0800789 133.0697368
134.0593947 135.0490526 136.0387105 137.0283684
138.0180263 139.0076842 139.9973421 More 1
2 1 3 6 6 9 12 18 17 26 22 27
47 36 44 47 53 61 60 62 74 70 81 93
73 105 80 88 67 50 56 35 30 19 7 8
5 2
Bin
Frequency

Histogram
Frequency 50 55.85714286 61.71428571 67.57142857
73.42857143 79.28571429 85.14285714 More 4
5 7 8 14 10 8 6
Bin
Frequency
Histogram
Frequency 75 78.14285714 81.28571429 84.42857143
87.57142857 90.71428571 93.85714286 More 2
5 10 12 14 11 5 3
Bin
Frequency
Histogram
Frequency 6 13.14285714 20.28571429 27.42857143
34.57142857 41.71428571 48.85714286 More 1
3 5 6 8 13 9 4
Bin
Frequency
Histogram
Frequency 6 13.14285714 20.28571429 27.42857143
34.57142857 41.71428571 48.85714286 More 1
3 5 6 8 11 11 4
Bin
Frequency
Histogram
Frequency 3 5.75 8.5 11.25 More 1 3 4
8 4
Bin
Frequency
Histogram
Frequency 6 7.75 9.5 11.25 More 1 2 10
5 2
Bin
Frequency
Histogram
Frequency 3 5.25 7.5 9.75 More 1 5 8 2

4
Bin
Frequency
Histogram
Frequency 3 4.25 5.5 6.75 More 1 3 7 6
3
Bin
Frequency
COURSE NAME: PRODUCTION MANAGEMENT
The students need to prepare the assignment individually in
essay format submitting only one pdf file.
1. Choose a specific production and operations system within a
specific industry.
2. Briefly explain the phases of PPC of at least one
process/product, identify the main drivers or factors that
determine its overall performance.
3. Identify the different elements that influences the quality of
this operational system.
4. Propose different measures and actions to take to enhance the
productivity and the quality of this system. Justify your answer.
Submission: Week 9, Sunday 28th of November 2021 at 23:59
CEST – Via Moodle (Turnitin).
Formalities:
· Wordcount for both assignments: 800 words.
· Cover, Table of Contents, References and Appendix are
excluded of the total wordcount.
· Font: Arial 12,5 pts.
· Text alignment: Justified.
· The in-text References and the Bibliography have to be in

Harvard’s citation style.
It assesses the following learning outcomes:
· Outcome 1: Understand the production management within
operation management.
· Outcome 2: Describe operations processes design and their
management to contextualize and improve production
performance.
· Outcome 3: Evaluate how to elaborate a master production
schedule MRS and material requirement planning MRP.
· Outcome 4: Understand quality control components and
measures.
Rubrics
Exceptional 90-100
Good 80-89
Fair 70-79
Marginal fail 60-69
Knowledge & Understanding (25%)
Student demonstrates excellent understanding of key concepts
and uses vocabulary in an entirely appropriate manner.
Student demonstrates good understanding of the task and
mentions some relevant concepts and demonstrates use of the
relevant vocabulary.
Student understands the task and provides minimum theory
and/or some use of vocabulary.
Student understands the task and attempts to answer the
question but does not mention key concepts or uses minimum
amount of relevant vocabulary.
Application (30%)
Student applies fully relevant knowledge from the topics
delivered in class.
Student applies mostly relevant knowledge from the topics
delivered in class.

Student applies some relevant knowledge from the topics
delivered in class. Misunderstanding may be evident.
Student applies little relevant knowledge from the topics
delivered in class. Misunderstands are evident.
Critical Thinking (30%)
Student critically assesses in excellent ways, drawing
outstanding conclusions from relevant authors.
Student critically assesses in good ways, drawing conclusions
from relevant authors and references.
Student provides some insights but stays on the surface of the
topic. References may not be relevant.
Student makes little or none critical thinking insights, does not
quote appropriate authors, and does not provide valid sources.
Communication (15%)
Student communicates their ideas extremely clearly and
concisely, respecting word count, grammar and spellcheck
Student communicates their ideas clearly and concisely,
respecting word count, grammar and spellcheck
Student communicates their ideas with some clarity and
concision. It may be slightly over or under the wordcount limit.
Some misspelling errors may be evident.
Student communicates their ideas in a somewhat unclear and
unconcise way. Does not reach or does exceed wordcount
excessively and misspelling errors are evident.
1
4
Unit VI Scholarly Activity
Michell Muldrow

Research Methods
Dr. Senft
November 6, 2021
Data Analysis: Hypothesis Testing
Independent Samples t Test: Hypothesis Testing
Ho4:There is no statistically significant difference in mean
Ha4:There is a statistically significant difference in mean scores
between prior training and revised training.
t-Test: Two-Sample Assuming Unequal Variances
Mean
69.79032258
84.77419355
Variance
122.004495
26.96456901
Observations
62
62
Hypothesized Mean Difference
0
df
87
t Stat

-9.666557191
P(T<=t) one-tail
9.69914E-16
t Critical one-tail
1.662557349
P(T<=t) two-tail
1.93983E-15
t Critical two-tail
1.987608282
The data shows a mean value of 69.79 for Group A and 84.77
for Group B. These p-value of 1.93 indicates that there is a
significant difference between the training programs. The p-
value 1.94 is considerably less than the alpha level of 0.05
which leads to a rejection of the null hypothesis. Therefore, the
alternative hypothesis is accepted which states that there is a
significant difference between the mean values between Group
A and group B.
Dependent Samples (Paired Samples) t Test: Hypothesis Testing
Ho5:There is no statistically significant difference in employee
blood lead levels between pre exposure and post exposure.
Ha5:There is a statistically significant difference in employee
t-Test: Paired Two Sample for Means

Pre-Exposure μg/dL
Mean
32.85714286
33.28571429
Variance
150.4583333
155.5
Observations
49
49
Pearson Correlation
0.992236043
Hypothesized Mean Difference
0
df
48
t Stat
-1.929802563
P(T<=t) one-tail
0.029776357
t Critical one-tail
1.677224196
P(T<=t) two-tail
0.059552714
t Critical two-tail
2.010634758

The data shows a mean value of 32.86 μg/dL for the Pre-
Exposure Group and 33.29 μg/dL for the Post-Exposure Group.
The mean values show a p-value of 0.059552714 > .05.
Therefore, the null hypothesis is accepted that there is no
statistically significant difference in mean values between the
pre-exposure and post-exposure in lead blood levels.
ANOVA: Hypothesis Testing
Ho6:There are no statistically significant differences in ROI
Ha6:There is a statistically significant differences in ROI
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
The data provided the average for Air which is 8.9, Soil which
is 9.1, Water which is 7, and
Training which is 5.4. The p-value of 1.76 < .05; we would
therefore reject the null hypothesis
and the alternate hypothesis will be accepted. There is a
statistically significant different mean
values for the between the Air , Soil , Water and Training. In
addition, it is not possible to tell
there the differences occur so in order to find that out we would
need to conduct a two-piece test.

1
4
Unit V Scholarly Activity
Michell Muldrow
Research Methods
Dr. Senft
November 2, 2021
Data Analysis: Hypothesis Testing
Correlation: Hypothesis Testing
particulate matter size and employee sick days.
Ha1:There is a statistically significant relationship between
microns
microns
1
-0.715984185
1
Regression Statistics

Multiple R
0.715984185
R Square
0.512633354
Adjusted R Square
0.507807941
Standard Error
1.327783455
Observations
103

ANOVA
df
SS
MS
F
Significance F
Regression
1
187.2953239
187.2953
106.2361758
1.89059E-17
Residual
101
178.0638994
1.763009
Total
102
365.3592233

Coefficients
Standard Error
t Stat
P-value
Lower 95%
Intercept
10.08144483
0.315156969
31.98865
1.16929E-54
9.456258184
microns
-0.522376554
0.050681267
-10.3071
1.89059E-17
-0.622914554
The Pearson correlation coefficient of r = -0.71 indicates a
moderately negative correlation. This equates to an r2 of
0.5126, explaining 50% of the variance between the variables.
Using an alpha of .05, the results indicate a p value of 1.89 <
.05. Therefore, the null hypothesis is rejected, and the
alternative hypothesis is accepted that there is a statistically
significant relationship between (PM) and annual sick
days(employee health).
Simple Regression: Hypothesis Testing
Ho2:There is no statistically significant relationship between
safety training expenditure and lost-time hours..
Ha2:There is a statistically significant relationship between

Multiple R
0.939559324
R Square
0.882771723
Adjusted R Square
0.882241279
Standard Error
24.61328875
Observations
223

ANOVA
df
SS
MS
F
Significance F
Regression
1
1008202.105
1008202
1664.210687
7.6586E-105
Residual
221
133884.8903
605.814
Total
222
1142086.996

Coefficients
Standard Error
t Stat
P-value
Lower 95%
Intercept
273.449419
2.665261963
102.5976
2.1412E-188
268.1968373
safety training expenditure
-0.143367741
0.003514368
-40.7947
7.6586E-105
-0.150293705
The multiple R is 0.93 which shows there is a strong positive
relationship between to two variables. The R square value of
0.8828 means that the regression model explains 88.28% of the
variation between safety training expenditure and lost time
hours. The ANOVA significance (F) value is 7.6586E-105. This
is way lesser than the alpha level of 0.05, meaning that there is
a statistically significant relationship between the two variables.
As such, we reject the null hypothesis and accept the alternative
hypothesis that there is a statistically significant relationship
between safety training expenditure and lost time hours.
Y= 273.44(intercept) +-(0.1433)(safety training)(X)
Multiple Regression: Hypothesis Testing
Ho3; There is no significant relationship between frequency,

angle in degrees, chord length, velocity, and displacement and
decibel level.
Ha3: There is a statistically significant relationship between
displacement and decibel level.
Multiple R
0.601841822
R Square
0.362213579
Adjusted R Square
0.360083364
Standard Error
5.51856585
Observations

1503
ANOVA
df
SS
MS
F
Significance F
Regression
5
25891.89
5178.378
170.0361
2.1289E-143
Residual
1497
45590.49
30.45457
Total

1502
71482.38
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Intercept
126.8224555
0.62382
203.2997
0
125.5988009
Frequency (Hz)
-0.0011169
4.76E-05
-23.4885
4.1E-104
-0.001210174
Angle in Degrees
0.047342353
0.037308
1.268957
0.204654
-0.025839288
Chord Length

-5.495318335
2.927962
-1.87684
0.060734
-11.23866234
Velocity (Meters per Second)
0.083239634
0.0093
8.950317
1.02E-18
0.064996851
Displacement
-240.5059086
16.51903
-14.5593
5.21E-45
-272.9088041
The multiple R value is 0.60 indicating a positive correlation
between the regression model and the dependent variable. The R
square value is 0.36622. This means that 36% of the variables in
DB can be explained by the entire set of the independent
variables(frequency, angle, chord length, velocity and
displacement).
The ANOVA F value of 2.1289. This is way lesser than the
alpha level of 0.05, meaning that there is a statistically
significant relationship between frequency, angle in degrees,
chord length, velocity, and displacement and decibel level. As
such, we reject the null hypothesis and accept the alternative
hypothesis that dB levels have a statistically significant
relationship with frequency, angle, chord length, velocity, and
displacement.
The regression model as an equation is as represented below:
Y = a0 + b1X1 + b2X2 + b3X3 + b4X4 + b5X5
y = 126.822 – (-0.001)(X1) + (0.047)(X2) -5.495(X3) +

0.083(X4) - 240.506(X5)
Where: X1 = frequency (Hz)
X2 = Angle in degrees
X3 = Chord Length
X4 = Velocity (m/s)
X5 = Displacement
Y = Decibels
References
Porterfield, T. (2017, May 18). Excel 2016 Correlation Analysis
[Video file]. Retrieved from
https://www.youtube.com/watch?v=kr64tfZmiGA
Seber, G. A., & Lee, A. J. (2012). Linear regression analysis
(Vol. 329). John Wiley & Sons.
1
9
Michell Muldrow
Research Methods
Dr. Senft
October 27, 2021

Data Analysis: Descriptive Statistics and Assumption Testing
The main assumptions of a parametric test include normality of
the distribution, where the histogram should show a symmetric
bell shape. The other assumption is the homogeneity of variance
and the linearity of the data.
Correlation: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
Histogram
Bin
Frequency
2
1
3
1

11
2
More
2
the normality is met since the data is symmetric. However, the
figure shows that more data in the datasets are skewed to the
right than those to the left.
Mean

7.126213592
Standard Error
0.186483898
Median
7
Mode
7
Standard Deviation
1.892604864
Sample Variance
3.58195317
Kurtosis
0.124922603
Skewness
0.142249784
Range
10
Minimum
2
Maximum
12
Sum
734
Count
103
Measurement Scale
because the dataset values cannot take a negative value, since
the smallest value is 2.
Measure of Central Tendency
The mean Sun Coast Remediation for this data is 7.1262139,
The skewness value for this data is 0.1422. Similarly, the

kurtosis value is 0.124922603. This therefore implies that the
data is slightly skewed to the right. However, the amount of
kurtosis values are both less than 0.5.
Evaluation
provided.
Simple Regression: Descriptive Statistics and Assumption
Testing
Histogram
Bin
Frequency
10
1

a mean of approximately 200. This implies that the assumption
of the normality is met since the data is symmetric. However,
the figure shows that more data in the datasets lies to the right
than those to the left.
lost time hours
Mean
188.0044843
Standard Error
4.803089447
Median
190
Mode
190
Standard Deviation
71.72542099
Sample Variance
5144.536016
Kurtosis
-0.50122353
Skewness
-0.08198487
Range
350
Minimum
10
Maximum
360
Sum
41925
Count

223
Measurement Scale
the smallest value is 10.
190 and a mode of 190. The range between the maximum and
the maximum value for this data is 350, with the maximum
value being 360 and the minimum value being 10.
kurtosis value is -0.50122353. This therefore implies that the
of skewness in the data is very little since the skewness and
kurtosis values are both between -0.5 and 0.5.
Evaluation
provided.
Multiple Regression: Descriptive Statistics and Assumption
Testing
Histogram
Bin
Frequency

103.38
1
104.3697
2
105.3593
1

106.349
3
107.3386
6
108.3283
6
109.3179

9
110.3076
12
111.2973
18
112.2869
17

113.2766
26
114.2662
22
115.2559
27

116.2456
47
117.2352
36
118.2249
44

119.2145
47
120.2042
53
121.1938
61
122.1835
60

123.1732
62
124.1628
74
125.1525
70

126.1421
81
127.1318
93
128.1214
73

129.1111
105
130.1008
80
131.0904
88

132.0801
67
133.0697
50
134.0594
56
135.0491
35

136.0387
30
137.0284
19
138.018
7

From the figure above, the histogram obtained is not bell -
shaped. This shows that the data is not normally distributed
with a mean of approximately 130. This implies that the
assumption of the normality is not met since the data is skewed
to the left.
Decibel
Mean
124.8359
Standard Error
0.177945
Median
125.721
Mode
127.315
Standard Deviation
6.898657
Sample Variance
47.59146
Kurtosis
-0.31419
Skewness
-0.41895
Range
37.607
Minimum
103.38
Maximum
140.987
Sum
187628.4
Count

1503
Measurement Scale
the smallest value is 103.38
125.721 and a mode of 127.315. The range between the
maximum and the maximum value for this data is 37.607, with
the maximum value being 140.987and the minimum value being
103.38.
kurtosis value is -0.31419. This therefore implies that the data
Evaluation
Independent Samples t Test: Descriptive Statistics and
Assumption Testing
Histogram
Bin
Frequency

50
4
55.85714
5
61.71429
7
67.57143
8

73.42857
14
79.28571
10
85.14286
8
More

6
the normality is met since the data is symmetric. However, the
figure shows that more data in the datasets lies to the left than
those to the right
Frequency

the normality is met since the data is symmetric.
Mean
69.79032258
Standard Error
1.402788093
Median
70
Mode
80
Standard Deviation
11.04556449
Sample Variance
122.004495
Kurtosis
-0.77667598
Skewness
-0.086798138
Range
41
Minimum
50
Maximum
91
Sum
4327
Count
62

Mean
84.77419355
Standard Error
0.659478888
Median
85
Mode
85
Standard Deviation
5.192741955
Sample Variance
26.96456901
Kurtosis
-0.352537913
Skewness
0.144084526
Range
22
Minimum
75
Maximum
97
Sum
5256
Count
62
Measurement Scale
Discuss measurement scale used here (e.g., nominal,
ordinal, interval, or ratio).
negative value, since the smallest value are 50 and 75.

being 75.
being 50.
The measures of central tendencies are therefore all relevant to
the data.
kurtosis value is -0.31419. This therefore implies that the data
Evaluation
being 75.
being 50.
the data.
The parametric test assumptions of linearity and normality were
met in the data.
Dependent Samples (Paired-Samples) t Test: Descriptive

Statistics and Assumption Testing
Histogram
Bin
Frequency
6
1
13.14286
3

20.28571
5
27.42857
6
34.57143
8
41.71429
13

left. This figure therefore that the data is not normally
the assumption of the normality is met since the data is not
symmetric.
Bin
Frequency
6
1

13.14286
3
20.28571
5
27.42857
6
34.57143
8

41.71429
11
48.85714
11
More
4

the assumption of the normality is met since the data is not
symmetric.
Pre-Exposure μg/dL
Mean
32.85714286
Standard Error
1.752306546
Median
35

Mode
36
Standard Deviation
12.26614582
Sample Variance
150.4583333
Kurtosis
-0.576037127
Skewness
-0.425109654
Range
50
Minimum
6
Maximum
56
Sum
1610
Count
49
Mean
33.28571429
Standard Error
1.781423416
Median
36
Mode
38
Standard Deviation
12.46996391
Sample Variance
155.5

Kurtosis
-0.654212507
Skewness
-0.483629097
Range
50
Minimum
6
Maximum
56
Sum
1631
Count
49
Measurement Scale
negative value, since the smallest values are both 6.
being 6.
being 6.
the data.
kurtosis value is -0.654212507. This therefore implies that the

of skewness in the data is large since the skewness and kurtosis
values are both less than -0.5.
Evaluation
From the above diagrams, the skewness and kurtosis values are
negative. Similarly, from the above histogram figures, it is
clearly shows that the datasets are skewed to the left, thus the
assumption of normality is not met. Similarly, since the
normality is not met, we conclude that the homogeneity
assumptions has not been met either.
ANOVA: Descriptive Statistics and Assumption Testing
Histogram
Bin
Frequency
3
1

the assumption of the normality is not met since the data is not
symmetric.
Bin
Frequency

normal. This figure therefore that the data is not normally
the assumption of the normality is met since the data is

Bin
Frequency
3
1
4.25
3
5.5
7

normal. This figure therefore that the data is not normally
the assumption of the normality is met since the data is
Measurement Scale
The measurement scale used in the data obtained is the is
nominal scale. This is because the data variables such as water,
soil and training cannot be categorized according to the order,
but rather are random labels whose ordering has no meaning.
8.9
0.684028
9
11
3.059068
9.357895
-0.6283
-0.36085
11
3
14
178
20
For the tables above, the mean value for this data variable is 8.9

B = Soil
Mean
9.1
Standard Error
0.390007
Median
9
Mode
8
Standard Deviation
1.744163
Sample Variance
3.042105
Kurtosis
0.11923
Skewness
0.492002
Range
7
Minimum
6
Maximum
13
Sum
182
Count
20
For the tables above, the mean value for this data for the
variable soil is 9.1 with a median of 9 and a mode of 8. The
range between the maximum and the maximum value for this
data is 7, with the maximum value being 13 and the minimum
value being 6.

Mean
5.4
Standard Error
0.265568
Median
5
Mode
5
Standard Deviation
1.187656
Sample Variance
1.410526
Kurtosis
0.253747
Skewness
0.159183
Range
5
Minimum
3
Maximum
8
Sum
108
Count
20
For the tables above, the mean value for variable training is 5.4
The skewness value for variable 1 is -0.36085. Similarly, the
kurtosis value is -0.6283. This therefore implies that the data is

slightly skewed to the left since the skewness and kurtosis
skewness in the data is small since the skewness value is greater
than -0.5.
The skewness value for the variable soil is 0.492002. Similarly,
the kurtosis value is 0.11923. This therefore implies that the
data is slightly skewed to the right since the skewness and
kurtosis values both have positive signs. However, the amount
of skewness in the data is small since the skewness value is less
than 0.5.
The skewness value for the variable soil is 0.76020. therefore,
implies that the data is skewed to the right since the skewness
have positive sign. However, the amount of skewness in the data
is large since the skewness value is greater than 0.5.
The skewness value for the variable training is 0.159183.
therefore, implies that the data is skewed to the right since the
skewness have positive sign. However, the amount of skewness
in the data is small since the skewness value is less than 0.5.
Evaluation
The parametric test assumption for the homogeneity and
normality are not met since the data values are skewed to the
right while some are skewed to the left. The linearity
assumption is not met either.
References
Include references here using hanging indentations.
Creswell, J. W., & Creswell, J. D. (2018). Research design:
Qualitative, quantitative, and mixed methods approaches (5th
ed.). SAGE.
Histogram
Frequency 103.38 104.3696579 105.3593158
106.3489737 107.3386316 108.3282895 109.3179474
110.3076053 111.2972632 112.2869211 113.2765789
114.2662368 115.2558947 116.2455526 117.2352105
118.2248684 119.2145263 120.2041842 121.1938421
122.1835 12 3.1731579 124.1628158 125.1524737

126.1421316 127.1317895 128.1214474 129.1111053
130.1007632 131.0904211 132.0800789 133.0697368
134.0593947 135.0490526 136.0387105 137.0283684
138.0180263 139.0076842 139.9973421 More 1
2 1 3 6 6 9 12 18 17 26 22 27
47 36 44 47 53 61 60 62 74 70 81 93
73 105 80 88 67 50 56 35 30 19 7 8
5 2
Bin
Frequency
Histogram
Frequency 50 55.85714286 61.71428571 67.57142857
73.42857143 79.28571429 85.14285714 More 4
5 7 8 14 10 8 6
Bin
Frequency
Histogram
Frequency 75 78.14285714 81.28571429 84.42857143
87.57142857 90.71428571 93.85714286 More 2
5 10 12 14 11 5 3
Bin
Frequency
Histogram
Frequency 6 13.14285714 20.28571429 27.42857143
34.57142857 41.71428571 48.85714286 More 1
3 5 6 8 13 9 4
Bin
Frequency
Histogram
Frequency 6 13.14285714 20.28571429 27.42857143
34.57142857 41.71428571 48.85714286 More 1
3 5 6 8 11 11 4
Bin
Frequency
Histogram
Frequency 3 5.75 8.5 11.25 More 1 3 4

8 4
Bin
Frequency
Histogram
Frequency 6 7.75 9.5 11.25 More 1 2 10
5 2
Bin
Frequency
Histogram
Frequency 3 5.25 7.5 9.75 More 1 5 8 2
4
Bin
Frequency
Histogram
Frequency 3 4.25 5.5 6.75 More 1 3 7 6
3
Bin
Frequency
Histogram
Frequency 2 3 4 5 6 7 8 9 10 11
More 1 1 5 13 18 24 18 12 7 2
2
Bin
Frequency
Histogram
Frequency 10 35 60 85 110 135 160 185 210
235 260 285 310 335 More 1 1 9 9 17
18 24 27 37 24 21 15 12 4 4
Bin
Frequency
1
6

Research Methods
Michell Muldrow
Research Methods
Dr. Senft
October 19, 2021
Research Methodology, Design, and Methods
This part comprises procedures or techniques for
identifying, selecting, and analyzing the topic under study.
Therefore, the methodology will enable the researcher to
critically examine a study's overall validity and reliability to
maintain evidence-based findings.
Research Methodology
The selected research methodology is quantitative. Using
this methodology, a researcher can use numbers and graphs to
express the collected data when confirming the theories and
assumptions about the research problem. Therefore, the
procedure enables an in-depth understanding of the relationship
between an independent and dependent variable in a population.
In sum, the primary reasons for selecting quantitative over
qualitative methods are that it is more scientific, objective, and
control-sensitive.
The methodology is more scientific than qualitative
because it involves collecting a large amount of data for
statistical analysis. As a result of this vast collection, bias gets
erased, and if more researchers ran the analysis on the data,
they get the same findings. Similarly, the quantitative

methodology gives a researcher more control over collecting
data while remaining objective to avoid bias (Basias & Pollalis,
2018). Based on these three reasons, the method becomes the
best alternative for the problem.
Research Design
For this project, the research design should be descriptive
(non experimental). This design will give the best results when
testing the research hypothesis on the six identified problems.
The design is helpful when you are describing a relationship
between two or more variables, all without any interference
from the researcher. For instance, in Sun Coast, the issue of
employee safety has inadequate training as the causing factor
for workplace injuries (effect). Therefore, this researcher will
examine the relationship between training and injuries
witnessed among the employees. These aspects will make the
critical variables for establishing the connection.
Research Methods/Data Collection Methods
The research methods that will be used for this project will
be descriptive, correlational, and causal-comparative.
Descriptive research often involves collecting informa tion
through data review, surveys, interviews, or observation.
Correlational Research is used to test a null hypothesis stating
no relationship exists between variables.
Causal-comparative research attempts to identify a cause-effect
relationship between two or more groups.
The data collection methods that will be used a survey in
which contact can be made via telephone which can include a
skype call or video conference, mail in which a questionare can
be sent out, electronically where a survey can be sent through
email ,observation in which a researcher can count the number
of people attending a certain event and finally document
analysis which uses public records to gather information.
Sampling Design
Sampling design is part of the research methodology, and
it considers the total number of Sun Coast employees as the
target finite population. Therefore, a sample will represent the

whole workforce population in which the people to make the
sample will be randomly selected. This random selection
implies that the researcher will give each employee an equal
probability of being chosen. Thus, a random sample becomes
the sampling design for this study. However, before selecting
the sample, a researcher will first define the population, specify
the sampling frame and unit, select the method, determine the
sample size, and specify the sampling plan (Burger & Silima,
2016). With Sun Coast the method of sampling will be target
population sample which is when samples are taken from a
person or population of interest to participate with each having
a chance to be chosen to provide a sample,
Sample size has to do with the size of the sample which would
be collected as it can have an affect of the quality of such
sample.
Sample type and stratified sample is a probability sample,
whereas a quota sampleand a convenience sample is a non-
probability
Data Analysis Procedures
P1- This problem will use a correlation analysis whch will
determine the existing association between the two variables by
computing their relationship. A high correlation will imply a
cause-effect relationship through this approach, while a low
correlation will mean a weak connection between the variables
(Tabuena & Hilario, 2021).
P2- This problem will use simple regression to determine
critical factors and the ones not critical to ignore.
P3- This problem will use multiple regression to determine if
additional research is needed or when multiple X variables are
included in the analysis to make a prediction about a change in
a single Y variable.
P4- This problem will use the t test a null hypothesis stating
there is no statistically significant difference between two

means.
P5- This problem will also use the t test determine whether the
mean difference between two sets of observations is zero.
P6- This problem will use a test that is like the t test however it
will determine if a null hypothesis that no statistically
significant differences exist among means for three or more
groups.
References
Basias, N., & Pollalis, Y. (2018). Quantitative and qualitative
research in business & technology: Justifying a suitable
research methodology. Review of Integrative Business and
Economics Research, 7, 91-105.
Burger, A., & Silima, T. (2016). Sampling and sampling design.
Journal of public administration, 41(3), 656-668.
Edmonds, W. A., & Kennedy, T. D. (2019). Quantitative
Methods for Experimental and Quasi-Experimental Research.
An Applied Guide to Research Designs: Quantitative,

Qualitative, and Mixed Methods. Thousand Oaks, CA: Sage, 29-
34.
Schweizer, M. L., Braun, B. I., & Milstone, A. M. (2016).
Research methods in healthcare epidemiology and antimicrobial
stewardship—quasi-experimental designs. Infection control &
hospital epidemiology, 37(10), 1135-1140.
Tabuena, A. C., & Hilario, Y. M. C. (2021). Research data
analysis methods in addressing the K-12 learning competency
on data analysis procedures among senior high school research
courses. International Journal of Recent Research and Applied
Studies, 8(3), 1.
Michell Muldrow
Research Methods
Dr. Senft
October 8, 2021

Employee safety is one of the crucial initiatives required
to increase employee performance, safety, and health.
Regarding the foundation set by the health and safety director,
the organization needs to establish the most effective techniques
to reduce losses incurred by the firm due to employee injuries at
work. With a focus on employee safety and welfare, the Sun
Coast project aims at developing effective strategies that protect
employees' health and wellbeing. Also, safety benefits the
organization in terms of reducing unnecessary financial costs
spent on employee injury.
Research objectives
The first project's objective is to determine the variation
of respiratory complications during pre-exposure and post-
exposure at the end of the remediation program. This objective
helps understand the exposure that presents more respiratory
risks than the other. The second aim is to establish if
employees' absenteeism is attributed to injuries resulting from
ineffective training. This second objective explores how
inadequate or ineffective training increases injury rates of
incidents, which contributes to workforce absenteeism (Gianino
et al., 2019). The third objective is to establish whether
standard earplugs are adequate to protect employees' ears if the
decibel levels are less than 120 decibels. It helps in knowing the
standard decibels for maintaining a healthy eardrum at the
workplace.
The fourth objective is to establish whether the new
training program is more effective than the earlier training
intervention. It enhances the comparison between the two
trainings to select the best one to implement in enhancing health
and safety at the workplace. The fifth objective is to explore the
variation of respiratory complications during pre-exposure and
post-exposure at the end of the remediation program. Through
this objective, the organization knows the exposure leading to

more severe complications than the other. The last objective is
to establish the existing differences in return on investment for
all lines of service. It helps determine the existing gap of return
on investment to make a good investment decision.
The other objective is to investigate the levels of
respiratory complications before and after remediation program
exposure; this will help identify the impact of the remediation
program on employees' respiratory complications incidences.
The last goal is establishing the effect of lost-time hours on the
general organizational performance. This goal with help
understands how lost time hours through sick leaves affect the
organization's revenue and profits.
Good research questions and hypotheses are developed
from identifying gaps and developing new ideas to fill the gaps
(Cai et al., 2019). Additionally, research questions must build
on the existing literature by recognizing its assumptions.
Research questions progress from the known facts to the
unknown statement that requires validation (Francis et al.,
2017). Similarly, the presented research questions and
hypotheses evaluate facts and the unknown factors to establish
solutions.
RO1: Determine if there is a relationship between PM size and
employe health program.
RQ1: Is there a relationship between particulate matter size and
employee sick days?
hypothesis.
RO2: Predict lost-time hours from training expenditures.
RQ2: Is there a relationship betwenn safety training expenditure
and lost-time hours?

hypothesis.
RO3: Predict the dB level of work environments.
RQ3: Is there a relationship between frequency ,angle in
degrees, chord length, velociy, and displacement and decible
level?
displacement and decibel level.
hypothesis.
RO4: Determine if the revised training program is more
RQ4: Is the revised new employee training program more
effective han the prior training program?
Ho4: There is no statistically significant difference in mean
Ha4: There is a statistically significant difference in
effectiveness between the new training program and the
previous one.
RO5: Determine if employee blood lead levels have increased.
RQ5: Have employee blood lead levels increased from their pre-
exposure baseline measurements?
Ho5: There is no statistically significant difference in employee
hypothesis.
RO6: Determine if the return on investment is the same for all
Sun Coast lines of service.
RQ6: Are there differences in return on investment between air
monitering, soil remediation, water reclamation, and health and
safety training?
Ho6: There are no statistically significant differences in ROI
hypothesis.

References
Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V.,
Cirillo, M., ... & Hiebert, J. (2019). Posing significant research
questions. Journal for Research in Mathematics Education,
50(2), 114-120.
Gianino, M. M., Politano, G., Scarmozzino, A., Stillo, M.,
Amprino, V., Di Carlo, S., ... & Zotti, C. M. (2019). Cost of
sickness absenteeism during seasonal influenza outbreaks of
medium intensity among health care workers. International
journal of environmental research and public health, 16(5), 747.
1
1
Literature Review
Name of Student
University
Course
Name of Instructor
Date
Literature Review
Particulate Matter (PM) Article
Occupational exposure to participate matter from air polluti on

Plos one, 15(1).
The article authors are al-Farabi Kazakh National
University, School of Public Health, Almaty, Kazakhstan,
National Research Tomsk State University; hence they qualify
to write it. Vinnikov et al., (2020), the primary purpose was to
study the occupational particulate matter's level in outdoor work
settings during the cold season. The study used AVOVA in data
analysis. Despite the research in Almaty, the same urban
landscape gives a similar concept regarding the association
between increases of particulate matter in the cold season. The
researchers established that M10 TWA lay between 0.050 to
2.075 mg/m3 with 0.366 as geometric mean and median 0.352
mg/m3, implying a high level of particulate matter. I believe
that the research will help implement ways to prevent pollutants
at work based on the research's evidence-based findings.
Safety Training Effectiveness
and duration of absence spells: proposal for a payroll driven
measure of absenteeism. Journal of occupational and
Environmental Medicine, 50(7), 840.
The above article, its authors, are affiliates of recognized
institutions of higher learning such as Yale University. The
study's purpose was to establish critical metrics for use in
determining the lost work time and duration of absences in work
resulting from training. The research utilized ANOVA in
determining the relationship between the work lost rate and
expenditures within a healthcare context. The findings showed
that hours not paid and absent days are significantly correlated
with the work loss rate. The research and Sun Coast aim at
establishing whether safety training can help reduce
absenteeism resulting from workplace injuries. The research
made a positive organizational impact in organizations can rely
on workforce databases to study the absenteeism patterns and
the leading cause and if these causing factors get attributed to

lack of training.
Sound-Level Exposure
conditions. Neonatal Network, 25(1), 25-
32.https://connect.springerpub.com/content/sgrnn/25/1/25.abstra
ct
The above article authors have acquired a masters' degree
and above from recognized universities. The research employed
a descriptive and comparative approach, and it used a
convenience sample of 134 babies. It was established that
respiratory therapy equipment, employee talking, alerts, and
infant fussiness lead to high sound levels. Also, the findings
showed that 4–6 dB is an effective sound level reduction
compared to noise levels that exceed 120 dB, as portrayed by
Dun Coast. The latter can protect workers' ears. Thus both the
research and Sun Coast want to establish the impact of high-
level sound on ears. Through this research, a positive
organizational impact is that Sun Coast's safety department to
rely on the evidence-based sound-reducing strategies that the
research proposes.
New Employee Training
in original equipment manufacturing companies on employee
perception of knowledge, behavior towards safety and safe work
environment. International Journal of Safety and Security
Engineering, 10(5), 689-
698.file:///C:/Users/user/Downloads/10.05_14.pdf
The article authors are affiliates of Deemed University.
The purpose of their study was toresearch the impact of safety
training on employees' practices or behaviors on safety and a
safe working environment. The study employed a survey
research design whereby 23 respondents participated in a pilot
survey. The researchers used a Cronbach alpha (α) to determine
the consistency of the questionnaire and SPSS vs. 21.0 (IBM) to
analyze the collected data. The results are that safety training

does not help in changing safety behaviors. Both the article and
Sun Coast aimed at finding whether safety training helps change
employees' safety behavior at the workplace (self- behavioral
change towards safety issues). This research will help Sun
Coast explore other ways of enhancing safety since safety
training seems ineffective based on the research findings.
Lead Exposure
https://www.sciencedirect.com/science/article/abs/pii/S0273230
020301240
All the authors are experts in occupational health and
safety and affiliates of the University of South Florida. The
main purpose of the research was to study the exposure profile
and compare it with the OSHA's construction lead standards.
The used method was comparative or quasi-Experimental to
help in establishing cause-effect relationships among various
exposures to lead. The findings revealed that laborers' and
painters' exposure to lead is greater than the set OSHA
construction lead standards. Both the research and Sun Coast
aim at establishing the risks associated with the workers' level
of lead exposure. Thus, I believe this research will help Sun
Coast differentiate between effective and ineffective lead
exposure controls or methods to ensure the safety of workers.
Return on Investment
Research.
Authors are affiliates of recognized universities such as
the University of New York and the University of Liverpool.
The research purpose was to justify the effect of investment
returns in profitability on stock prices. The researchers used
online data in IDX for data collection. The findings showed that

13Sun Coast Remediation ProjectMichell Mul

13Sun Coast Remediation ProjectMichell Mul

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