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Table of Contents
Contents
Executive Summary 3
Introduction 4
Statement of the Problems 4
Literature Review 7
Research Objectives 9
Research Questions and Hypotheses 9
Research Methodology, Design, and Methods 11
Research Methodology 11
Research Design 11
Research Methods 11
Data Collection Methods 12
Sampling Design 12
Data Analysis procedures 12
Data Analysis: Descriptive Statistics and Assumption Testing 13
Measure of central tendency. 15
Simple Regression: Descriptive Statistics and Assumption

Testing 16
Measure of central tendency. 18
ANOVA: Descriptive Statistics and Assumption Testing 28
Data Analysis: Hypothesis Testing 31
Dependent Samples (Paired Samples) t Test: Hypothesis Testing
32
ANOVA: Hypothesis Testing 33
Findings 34
Recommendations 34
References 35
Executive Summary
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 six business problems identified
include particulate matter, safety training effectiveness, sound
level exposure, lead exposure, new employee training, and
return on investment. The literature review was based on these
six problems. The research methodology used is quantitative
since it allows for computation and experimentation. Besides, it

is good for quantitative behavior. The main null hypothesis
(Ho) being analyzed is that there is no statistical difference
between individual groups that have undergone training, and
those that have not undergone training. The independent t-test
indicates statistically significant performance results between
group A and group B. This test results in a p-value of less than
0.05, which is 1.94E-15. The study results showed that new
employees who received training on safety could apply these
measures throughout. Besides, exposure to particulate matter
leads to ill health. The toxicity differs in different types of
particulate matter. Lead poisoning was detected in some of the
employees working in some sites. The company should increase
its level of monitoring employees working in various sites for
lead exposure. That will make it possible for such employees to
be detected early enough so that interventions can be taken.
Besides, employees should be given personal protective
equipment to protect them from exposure to toxic particulate
matter. Such equipment includes face masks.
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. Senior leadership is interested in
seeing the project through to fruition. The following is the
completion of that project and includes the statement of the
problems, literature review, research objectives, research
questions and hypotheses, research methodology, design, and
methods, data analysis, findings, and recommendations.
Statement of the Problems
Six business problems were identified:
Particulate Matter (PM)
There is a concern that job-site particle pollution is adversely
impacting employee health. Although respirators are required in
certain environments, PM varies in size depending on the

project and job site. PM that is 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 that is less than 2.5
microns can float in the air for hours to weeks (e.g. bacteria,
viruses, oil smoke, smog, soot). Due to the smaller size of PM
that is less than 2.5 microns, it is potentially more harmful than
PM that is between 10 and 2.5 since the conditions are more
suitable for inhalation. PM that is less than 2.5 is 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. PM air quality data have been collected from
103 job sites, which is recorded in microns. Data are also
available for average annual sick days per employee per job-
site.
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.
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 ear-plugs 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. 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 procurement of appropriate ear

protection for employees.
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: Group A employees who
participated in the prior training program and 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.
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 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 2-year lead remediation project. It is necessary to
determine if blood lead levels have increased.
Return on Investment
Sun Coast offers four lines of service 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 lines of
service, it would be helpful to know where differences exist.

Literature Review
The literature review for the topic is organized as follows:
Particulate Matter (PM)
The author of the article “Particulate matter components,
sources, and health: Systematic approaches to testing effects.”
is known as Kate, who is a scientist, Daniel, who is President
and Annemoon, who is a scientist at HEI. The purpose of the
study of the article is to know if some items and sources of
particulate matter are more toxic than others
("Tandfonline.com", 2020). The article relates to the Sun Coast
problem because it researches on the health impacts due to
exposure to particulate matter in order to protect the public
health.
Safety Training Effectiveness
The author of the article is Mat Naim Abdullah Mohd Asmoni,
is a senior writer serving in the Department of Property
Management. He has a BSc. Quantity Surveying as well. The
purpose of the study is to provide information on methods of
effective training that should be applied in industries to ensure
employees understand their part effectively (Asmoni, 2020).
The article relates to the Sun Coast problem because it can
provide ways of according effective safety training for newly
employed workers. The article reviews the best methods that
can be applied by organizations to ensure trainings meet the
objectives and goals.
Sound Level Exposure
The author of the article “Assessment of Occupational Noise
Exposure among Groundskeepers in North Carolina Public
Universities” is Jo Anne G. Balanay. She works in
Environmental Health Sciences Program and heads the DHE in
The University of East Carolina. The purpose of the study is to
assess the exposure of people to various forms of noise in
various institutions and determine the relationship between the
noise exposure and the variables (Balanay, 2020).

This article relates to the Sun Coast problem of Sound-level
Exposure because it can provide means of assessing how the
noise is affecting the employees with regards to the presented
variables of the research.
New Employee Training
The author of the article “The Effect of Training and
Development on Employee Attitude as it relates to Training and
Work Efficiency” is Debra L. Truitt, who is MD in the
Salisbury University and has education qualifications on
management. The purpose of the study is to explore the
connection between the experiences and attitudes of training
about perceived job proficiency. According to the article, new
training and development on workers ensures work efficiency
(Truitt, 2020). It applies to the Sun Coast problem of new
employee training since by training the incoming employees,
their efficiency in their activities is likely to increase. It also
provides ways for assessing the effect of employee training on
their job outcomes.
Lead Exposure
The author of the article “Lead toxicity with a new focus” is
Ireka Buka, who works in the section Of Pediatric
Environmental Health Section. The purpose of the research is to
detect the impact of lead exposure and lead toxicity in the
society (Society, 2020). This article applies to the problem of
Lead Exposure by providing ways of testing the presence of
lead in the blood and outlining its effects on the Sun Coast
project especially to the workers that are present in the work
sites.
Return on Investment
The author of the article “A Study on Return on Investment of
Training Programme in a Government Enterprise” is Priya
Dhamija Gupta and has an MBA and PhD in Human resource.
The article provides ways of calculating return on investment on
training programs using various evaluation models (Gupta,
2020). Therefore, Sun Coast project can estimate the return on

investment using the different ways provided in this article to
know how much they will get on investing in various
things.Research Objectives
The following were the objectives of the research:
RO1: To determine if there is a relationship between the size of
PM and the health of employees.
RO2: To identify whether safety training has been effective in
reducing the time lost in hours.
RO3: To determine the dB levels in job sites before sending
employees to work there.
RO4: To determine if the new employee training program is
more effective than the previous one.
RO5: To determine whether the blood lead levels in employees
have increased in order to determine the level of exposure to
lead.
RO6: To identify if there is a difference in the return on
investment on the company’s lines of service.Research
Questions and Hypotheses
This research aimed at measuring the following hypotheses and
answering the following research question:
RQ1: Is there a relationship between the size of PM and the
health of employees?
H01: There is no relationship between the size of PM and the
health of employees.
HA1: There is a significant relationship between the size of PM
and the health of employees.
RQ2: Has the safety training been effective in reducing the time
lost in hours?
H02: The safety training has not been effective in reducing the
time lost in hours.
HA2: The safety training has caused a significant reduction in
the time lost in hours.
RQ3: Does the dB level in the job site exceed the standard
12decibels?

H03: The dB level does not exceed 120 decibels in the job site.
HA3: There is evidence that the dB level exceeds 12o decibels
in the job site.
RQ4: Is the new employee training program more effective than
the previous one?
H04: The new employee training program is not as effective as
the previous one.
HA4: There is significant evidence that the new employee
training program is more effective than the previous one.
RQ5: Have the blood lead levels in employees increased?
H05: There is no evidence of increase in blood lead levels in
employees.
HA5: There is evidence of increase in blood lead levels in
employees.
RQ6: Is there a difference in the return on investment among
the company’s lines of service?
H06: There is no notable difference in the return on investment
among the company’s lines of service.
HA6: There is a significant difference in the return on
investment among the company’s lines of service.Research
Methodology, Design, and Methods
Research Methodology
The research methodology is quantitative. Two reasons support
the use of the methodology over the qualitative approach. First,
the method is best for quantifying behavior, attitudes, opinions,
and other variables and generalize a large study population. The
method is ideal for computation and experimentation and since
all of the research questions can be answered by
experimentation, the quantitative approach is the most ideal.
Secondly, the primary goal of the quantitative methodology is
to understand the relationship between the dependent and
independent variables (Bryman, 2017). The research objectives

are tied to how independent and dependent variables relate thus
the methodology will off the best explanation of the
relationships.
Research Design
The research design will be the causal research which is also
known as the explanatory research design. The design focuses
on the investigation of cause and effect relationships. Based on
the research objectives, it will be necessary that variables that
are assumed to cause change will be observed (Kemp et al.,
2018). The causal research design will help explain if the
change in the variables is related to variations in the variables
under study. The causal research design will help explain the
relationship between the independent and dependent variables.
Research Methods
Different research methods will be used to test the seven
hypotheses. For hypothesis 1, the experimentation method will
be the most ideal as it will make it possible to test if an increase
in PM size influences the health of an employee. For hypothesis
2, the correlation method will be the best. The method will
allow the researcher to see if there is a relation between safety
training and lost hours. For hypothesis 3, the descriptive method
will be the best. The method will make it possible to assess
whether the dB level in a job site exceeds the standard
12decibels. For hypothesis 4, the casual comparative method
will be the best as it will make it easy to confirm if the training
program causes an improvement or not. It will also make it
possible to compare the previous training program and the new
program. For hypothesis 5, the descriptive method will be the
best. The method will make it possible to assess whether the
blood lead levels in employees have increased by simply
looking at the available data. For the last hypothesis, the
descriptive method will be the best. The method will make it
possible to assess if there a difference in the return on

investment among the company’s lines of service by looking at
the available statistics on the lines of services.
Data Collection Methods
For hypothesis 1, the best data collection method will be record
analysis as records on the size of PM and the health of
employees already exists. The best data collection method for
hypothesis 2 records analysis as records on safety training and
records on the time lost in hours exists. The best data collection
method for hypothesis 3 is observation as it will be easy to
observe whether the dB level in the job site exceeds the
standard 12decibels. The best data collection method for
hypothesis 4 is the survey method as it will require the
employees to share whether they believe the training was
effective or not. The best data collection method for hypothesis
5 is records analysis. Confirming or dispelling the hypothesis
only needs analysis of the existing data. The best data
collection method for the last hypothesis is records analysis as
only the existing data on the lines of services is analyzed.
Sampling Design
The best sampling method for the research is random sampling.
Random sampling has two main advantages that make it ideal.
First, it eliminates bias, the method will reduce the chance of
influencing the study. Secondly, the random sampling method is
ideal for a large population (Sharma, 2017). The chosen samples
represent the whole population. The method will be ideal for the
study considering the study population is huge. The company
has many employees.
Data Analysis procedures
For hypothesis 1, the regression analysis will be the most ideal
as it allows the analysis of relationships between two or more
variables (Ahlgren & Walberg, 2017). It will allow the analysis
of PM size and the health of patients. For hypothesis 2, the t-
test will be used as it makes it possible to analyze the

significant difference between the two groups. For hypothesis 3,
the regression analysis will be used as it will allow the analysis
of the DB level and noise. For hypothesis 4, the ANOVA
analysis will be used. The ANOVA analysis method is ideal for
identifying differences between three independent groups
(Boisgontier & Cheval, 2016). In testing hypothesis 4, one has
several groups; the trained groups, the training, and levels of
understanding. For hypothesis 5, the regression test will be used
as it allows the analysis of relationships between two or more
variables. For the last hypothesis, the ANOVA analysis will be
used as the difference between three or more groups is being
checked.Data Analysis: Descriptive Statistics and Assumption
Testing
Data Analysis: Descriptive Statistics and Assumption Testing
Correlation: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Bin
Frequency
0-5
65
6-10
137
11-15
4
More
0
Histogram.
Descriptive statistics table.

Microns
mean annual sick days per employee
Mean
5.657281553
Mean
7.126213592
Standard Error
0.255600143
Standard Error
0.186483898
Median
6
Median
7
Mode
8
Mode
7
Standard Deviation
2.59405814
Standard Deviation
1.892604864
Sample Variance
6.729137636
Sample Variance
3.58195317
Kurtosis
-0.852161902
Kurtosis
0.124922603

Skewness
-0.373257127
Skewness
0.142249784
Range
9.8
Range
10
Minimum
0.2
Minimum
2
Maximum
10
Maximum
12
Sum
582.7
Sum
734
Count
103
Count
103
Largest(1)
10
Largest(1)
12
Smallest(1)
0.2
Smallest(1)
2

Measurement scale.
Ratio scale was used since it has all of the features of interval
scale and a true zero, which refers to complete absence of the
characteristic being measured. This scale was used because
microns refers to length which can be effectively described
using ratio scale. Also, the mean annual sick days per employee
is a number that is well described using the ratio scale
(Peacock, 2019).
Measure of central tendency.
From the above descriptive statistics table, the mean, mode, and
median for mean annual sick days per employee are;
Mean: 7.126213592
Medium: 7
Mode: 7
Also for the Microns, the mean, mode, and medium are;
Mean: 5.657281553
Medium: 6
Mode: 8
Evaluation.
From the descriptive statistics, for microns, the skewness was -
0.373257127 while the kurtosis was -0.852161902, on the other
hand, for mean annual sick days per employee, skewness was
0.142249784 and kurtosis was 0.124922603, it must be noted
that for normality test assumption, the skewness must be within
a +-2 range while kurtosis between +-7 range. However
according to the descriptive statistical values above, the
skewness and kurtosis ranges aren’t within the intended range
for normality assumption qualification. Therefore the
parametric statistical testing were not met.
Simple Regression: Descriptive Statistics and Assumption
Testing
Bin

Frequency
0-500
331
501-1000
76
1001-1500
27
1501-2000
11
2001-2500
1
More
0
Histogram.
safety training expenditure
lost time hours
Mean
595.9843812
Mean
188.0044843
Standard Error
31.4770075
Standard Error
4.803089447

Median
507.772
Median
190
Mode
234
Mode
190
Standard Deviation
470.0519613
Standard Deviation
71.72542099
Sample Variance
220948.8463
Sample Variance
5144.536016
Kurtosis
0.444080195
Kurtosis
-0.501223533
Skewness
0.951331922
Skewness
-0.081984874
Range
2251.404
Range
350
Minimum
20.456
Minimum
10
Maximum
2271.86
Maximum
360

Sum
132904.517
Sum
41925
Count
223
Count
223
Largest(1)
2271.86
Largest(1)
360
Smallest(1)
20.456
Smallest(1)
10
Measurement scale.
safety training expenditure refers to length which can be
effectively described using ratio scale. Also, the lost time hours
are numbers that are well described using the ratio scale
(McCarthy, R. McCarthy, M, Ceccucci, & Halawi, 2019).
median for safety training expenditure are;
Mean: 595.9843812
Medium: 507.772
Mode: 234
For the lost time hours, the mean, mode, and medium are;
Mean: 188.0044843
Medium: 190

Mode: 190
Evaluation.
From the descriptive statistics, for safety training expenditure,
the skewness was -0.951331922 while the kurtosis was
0.444080195, on the other hand, for lost time hours, skewness
was -0.081984874 and kurtosis was -0.501223533, it must be
noted that for normality test assumption, the skewness must be
within a +-2 range while kurtosis between +-7 range. However
parametric statistical testing were not met (Ma’arif, Motahar, &
Mohd Satar, 2018).
Multiple Regression: Descriptive Statistics and Assumption
Testing
Bin
Frequency
0-20
1503
21-40
761
41-60
277
61-80
465
More
0
Histogram.

Chord Length
Velocity
Mean
0.116140053
Mean
50.86074518
Standard Error
0.001256368
Standard Error
0.401686079
Median
0.1176
Median
39.6
Mode
0.0917
Mode
39.6
Standard Deviation
0.048707555
Standard Deviation
15.5727844
Sample Variance
0.002372426
Sample Variance
242.5116138
Kurtosis
-1.178196484
Kurtosis
-1.563951274

Skewness
-0.027537436
Skewness
0.235852414
Range
0.1697
Range
39.6
Minimum
0.03
Minimum
31.7
Maximum
0.1997
Maximum
71.3
Sum
174.5585
Sum
76443.7
Count
1503
Count
1503
Largest(1)
0.1997
Largest(1)
71.3
Smallest(1)
0.03
Smallest(1)
31.7
median for Chord Length are;

Mean: 0.116140053
Medium: 0.1176
Mode: 0.0917
For the Velocity, the mean, mode, and medium are;
Mean: 50.86074518
Medium: 39.6
Mode: 39.6
Evaluation.
From the descriptive statistics, for Chord Length, the skewness
was -0.027537436 while the kurtosis was -1.178196484, on the
other hand, for Velocity, skewness was 0.235852414 and
kurtosis was -1.563951274, it must be noted that for normality
test assumption, the skewness must be within a +-2 range while
kurtosis between +-7 range (Qiu, Wei, & Bai, 2017). However
parametric statistical testing were not met.
Independent Samples t Test: Descriptive Statistics and
Assumption Testing
Bin
Frequency
0-10
0
11-20
0
21-30
0
31-40
0
41-50
4
51-60
8
61-70

20
71-80
35
81-90
48
91-100
9
More
0
Histogram.
Group A Prior Training Scores
Group B Revised Training Scores
Mean
69.79032258
Mean
84.77419355
Standard Error
1.402788093
Standard Error
0.659478888

Median
70
Median
85
Mode
80
Mode
85
Standard Deviation
11.04556449
Standard Deviation
5.192741955
Sample Variance
122.004495
Sample Variance
26.96456901
Kurtosis
-0.77667598
Kurtosis
-0.352537913
Skewness
-0.086798138
Skewness
0.144084526
Range
41
Range
22
Minimum
50
Minimum
75
Maximum
91
Maximum
97

Sum
4327
Sum
5256
Count
62
Count
62
Largest(1)
91
Largest(1)
97
Smallest(1)
50
Smallest(1)
75
Measurement scale.
Group A Prior Training Scores can be treated as student marks
which can be treated as measurement values which can be
effectively described using ratio scale. Also, the Group B
Revised Training Scores can as well as classified as values that
are well described using the ratio scale (Stypulkowski,
Bernardeau, & Jakubowski, 2018).
median for Group A Prior Training Scores are;
Mean: 69.79032258
Medium: 70
Mode: 80
For the Group B Revised Training Scores, the mean, mode, and
medium are;
Mean: 84.77419355

Medium: 85
Mode: 85
Evaluation.
From the descriptive statistics, for Group A Prior Training
Scores, the skewness was --0.086798138 while the kurtosis was
-0.77667598, on the other hand, for Group B Revised Training
Scores, skewness was 0.144084526 and kurtosis was -
0.352537913, it must be noted that for normality test
assumption, the skewness must be within a +-2 range while
kurtosis between +-7 range. However according to the
descriptive statistical values above, the skewness and kurtosis
ranges aren’t within the intended range for normality
assumption qualification. Therefore the parametric statistical
testing were not met (Wang, et al. 2019).
Dependent Samples (Paired-Samples) t Test: Descriptive
Statistics and Assumption Testing
Bin
Frequency
0-10
6
11-20
12
21-30
18
31-40
28
41-50
32
51-60
2
More
0

Histogram.
Pre-Exposure μg/dL
Post-Exposure μg/dL
Mean
32.85714286
Mean
33.28571429
Standard Error
1.752306546
Standard Error
1.781423416
Median
35
Median
36
Mode
36
Mode
38
Standard Deviation
12.26614582
Standard Deviation
12.46996391
Sample Variance
150.4583333
Sample Variance

155.5
Kurtosis
-0.576037127
Kurtosis
-0.654212507
Skewness
-0.425109654
Skewness
-0.483629097
Range
50
Range
50
Minimum
6
Minimum
6
Maximum
56
Maximum
56
Sum
1610
Sum
1631
Count
49
Count
49
Largest(1)
56
Largest(1)
56
Smallest(1)
6
Smallest(1)

6
Measurement scale.
characteristic being measured. This scale was used because Pre-
Exposure μg/dL parameters can be treated as measurement
values which can be effectively described using ratio scale.
Also, the Pre-Exposure μg/dL refers to measurement values that
are well described using the ratio scale (Berghoff, et al. 2016).
median for Pre-Exposure μg/dL are;
Mean: 32.85714286
Medium: 35
Mode: 36
For the Post-Exposure μg/dL, the mean, mode, and medium are;
Mean: 33.28571429
Medium: 36
Mode: 38
Evaluation.
From the descriptive statistics, for Pre-Exposure μg/dL, the
skewness was -0.425109654 while the kurtosis was -
0.576037127, on the other hand, for Post-Exposure μg/dL,
skewness was -0.483629097 and kurtosis was -0.654212507, it
must be noted that for normality test assumption, the skewness
must be within a +-2 range while kurtosis between +-7 range.
However according to the descriptive statistical values above,
the skewness and kurtosis ranges aren’t within the intended
range for normality assumption qualification. Therefore the
parametric statistical testing were not met (Barkana, Saricicek,
& Yildirim, 2017).ANOVA: Descriptive Statistics and
Assumption Testing
Bin

Frequency
0-5
21
6-10
45
11-15
14
More
0
Histogram.
A = Air
B = Soil
C = Water
D = Training

Mean
8.9
Mean
9.1
Mean
7
Mean
5.4
Standard Error
0.68
Standard Error
0.39
Standard Error
0.58
Standard Error
0.3
Median
9
Median
9
Median
6
Median
5
Mode
11
Mode
8
Mode
6
Mode
5
Standard Deviation
3.06
Standard Deviation
1.74

Standard Deviation
2.58
Standard Deviation
1.2
Sample Variance
9.36
Sample Variance
3.04
Sample Variance
6.63
Sample Variance
1.4
Kurtosis
-0.6
Kurtosis
0.12
Kurtosis
-0.2
Kurtosis
0.3
Skewness
-0.4
Skewness
0.49
Skewness
0.76
Skewness
0.2
Range
11
Range
7
Range
9
Range
5

Minimum
3
Minimum
6
Minimum
3
Minimum
3
Maximum
14
Maximum
13
Maximum
12
Maximum
8
Sum
178
Sum
182
Sum
140
Sum
108
Count
20
Count
20
Count
20
Count
20
Largest(1)
14
Largest(1)
13

Largest(1)
12
Largest(1)
8
Smallest(1)
3
Smallest(1)
6
Smallest(1)
3
Smallest(1)
3
Measurement scale.
In this statistical descriptive analysis, the nominal scale have
been used. In this case, the letters that is A, B, C and D have
been used to identify or denote items. For instance, A has been
used to denote Air, B has been used to denote Soil, and C have
been used to denote Water and finally, D have been used to
denote Training. However, it should be noted that the letters
aren’t used to denote the specialty of an item in relation to
another, they are just applied for identity (Innocenti, et al.
2017).
median for air are;
Mean: 8.9
Medium: 9
Mode: 11
Also for the soil, the mean, mode, and medium are;
Mean: 9.1
Medium: 9

Mode: 8
For water, the mean, mode, and medium are;
Mean: 7
Medium: 6
Mode: 6
For the training, the mean, mode, and medium are;
Mean: 5.4
Medium: 5
Mode: 5
Evaluation.
From the descriptive statistics, for Air, the skewness was -0.4
while the kurtosis was -0.6, on the other hand, for Soil,
skewness was 0.49 and kurtosis was 0.12, also for Water, the
skewness was 0.76 while the kurtosis was -0.2, on the other
hand, for Training, skewness was 0.2 and kurtosis was 0.3 it
must be noted that for normality test assumption, the skewness
must be within a +-2 range while kurtosis between +-7 range.
However according to the descriptive statistical values above,
the skewness and kurtosis ranges aren’t within the intended
range for normality assumption qualification. Therefore the
parametric statistical testing were not met (Peacock, 2019).Data
Analysis: Hypothesis Testing
Independent Samples t Test: Hypothesis Testing
The main null hypothesis (Ho) being analyzed is that there
is no statistical difference between individual groups that have
undergone training and those that have not undergone training.
The independent t-test indicates a statistically significant
performance results between group A and group B. This test
results a p value of less than 0.05 which is 1.94E-15. It is worth
noting that the p value is usually used to the measure the level
of statistical significance between various variables (Nahm,
2017 p.241). This provision can be attributed to the fact that the
performance of the groups is impacted by the level of training
thus opposing the H0 hypothesis which states that there is no
statistical difference between the two groups despite the
training.

Ha: There is a statistically significant difference in the scores
between Group A training scores and Group B testing scores.
t-Test: Two-Sample Assuming Unequal Variances
Group A Prior Training Scores
Group B Revised Training Scores
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
Dependent Samples (Paired Samples) t Test: Hypothesis Testing
Ho: There is no statistically significant difference between the
impact on employee before and after the exposure.
t-Test: Paired Two Sample for Means
Pre-Exposure μg/dL
Post-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
According to the paired t-test, there seems to be significant
effect before and after exposure. This provision is supported by
statistical findings p (T<=t) = 0.0297764 which indicates a very
significant difference between the two test results.
impact on employee before and after the exposure. This
hypothesis is rejected according the statistical analysis.
Ha: There is no statistically significant difference between the
impact on employee before and after the exposure which is
indicated by the low p value.ANOVA: Hypothesis Testing
impact on employee before and after the exposure.
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
60.93333
11.9231
1.76E-06
2.724944
Within Groups
388.4
76
5.110526
Total
571.2
79
According to the one-way ANOVA analysis, there is
significant difference between the various types of project
returns. Their significance can be observed from the variance an
average mean outputs as well as from the various coefficients

used in the ANOVA analysis table.
impact on employee before and after the exposure. The analysis
indicates a p-value of 1.75887702754493E-06 which is below
the p-value of 0.5 that which is against the null hypothesis that
indicates that there are no significant differences between the
return on investments of the different groups of investments.
Ha: There is statistically significant difference between the
impact on employee before and after the exposure which is an
acceptable hypothesis based on the ANOVA analysis
results.Findings
The results of the study showed that new employees who
received training on safety could apply these measures
throughout. Besides, exposure to particulate matter leads to ill
health. The toxicity differs in different types of particulate
matter. Lead poisoning was detected in some of the employees
working in some sites.
Recommendations
The company should increase its level of monitoring employees
working in various sites for lead exposure. That will make it
possible for such employees to be detected early enough so that
interventions can be taken. Besides, employees should be given
personal protective equipment to protect them from exposure to
toxic particulate matter. Such equipment includes face masks.
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