Review the Introduction and statement of problems for familiarization. Most of the sections have been completed from previous work that is referenced in the template (Unit VII – Templ.pdf).
Complete the sections highlighted
- Executive Summary
- Recommendations
- References (all 6 sections)
This assignment should be no less than three pages in length, follow APA-style formatting and guidelines, and use references and citations as necessary.
Insert Title Here
Insert Your Name Here
Insert University Here
Course Name Here
Instructor Name
Date
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Table of Contents
Contents
Executive Summary ………………………………………………………………………………………………………… 3
Introduction ……………………………………………………………………………………………………………………. 4
Statement of Problems …………………………………………………………………………………………………….. 4
Literature Review……………………………………………………………………………………………………………. 6
Research Objectives ………………………………………………………………………………………………………… 9
Research Questions and Hypotheses ……………………………………………………………………………….. 10
Research Methodology, Design, and Methods ………………………………………………………………….. 11
Research Methodology ……………………………………………………………………………………………….. 11
Research Design ………………………………………………………………………………………………………… 12
Research Methods ……………………………………………………………………………………………………… 12
Data Collection Methods …………………………………………………………………………………………….. 12
Sampling Design ……………………………………………………………………………………………………….. 13
Data Analysis Procedures……………………………………………………………………………………………. 13
Data Analysis: Descriptive Statistics and Assumption Testing ……………………………………………. 14
Data Analysis: Hypothesis Testing ………………………………………………………………………………….. 29
Findings……………………………………………………………………………………………………………………….. 36
Recommendations …………………………………………………………………………………………………………. 37
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Executive Summary The executive summary will go here. The paragraphs are not indented, and it should be
formatted like an abstract. The executive summary should be composed after the project is
complete. It will be the final step in the project. Delete instructions and examples highlighted in
yellow before submitting this assignment.
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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
statement of the problems, literature review, research objectives, research questions and
hypotheses, research methodology, design, and methods, data analysis, findings, and
recommendations.
Statement of 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, 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 the smaller size of PM less than 2.5 microns, it is potentially more harmful than PM
between 10 and 2.5 since the conditions are more suitable for inhalation. PM less than 2.5 are
also able to be inhaled into the deeper regions of the lungs, potentially causing more deleterious
health effects. It would be helpful to understand if there is a relationship between PM size and
employee health. 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
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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 earplugs are adequate to protect employee hearing if the decibel levels are less
than 120 decibels (dB). For environments with noise-levels exceeding 120 dB, more advanced
and expensive hearing protection is required, such as earmuffs. 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; 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.
Lead Exposure
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Employees working on job sites to remediate lead must be monitored. Lead levels in
blood are measured as micrograms of lead per deciliter of blood (μg/dL). A base-line 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.
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 Health and safety are crucial in managing the risks to protect the employees and the
organization. Researchers attribute workers’ safety environment and culture to managerial
actions and strategies. Given the critical nature of the work environment at Sun Coast, where
employees work in mines to extract toxic substances, safety issues remain core to the
contraction, compensation and long-term litigation. As the safety director at Sun Coast,
evaluation of safety matters narrows us to the following key issues.
Particulate Matter (PM) Article
McClellan (2016) undertook an approach that involved quantitative analysis that on a
microscale exposed a number of sources on particulate matter and showcased a very strong
relationship between the high concentration of PM2.5 and adverse affects it caused. This was
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validated through a study that was took place between 1970 and 2010, where it showed that the
effects of PM2.5 went down compared to prior years. The crude death rate has also gone down
over time. The death rate in the United States shows that the health effects of PM2.5 are not all
that big when compared to the total number of diseases.
Safety Training Effectiveness Article
Qualitative data from Norris, Spicer, and Byrd (2019) illustrate that the annual 10-hour
safety training courses in the construction sector are inadequate and result in a significant
number of accidents. VR-based safety training helps businesses effectively exercise detecting
and preventing hazards while instructing employees what it’s like to lose without placing them in
risk of losing their lives. Norris, Spicer, and Byrd (2019, p. 37) claimed that the results of this
study “showed that VR training was more effective than traditional training methods in a number
of ways. Researchers found that higher levels of engagement and opportunities for direct, safe
exposure to dangerous environments make it possible for trainees to interact with each other and
get feedback in real time, which enables them learn more.
Sound-Level Exposure Article
Hearing loss is the third most common long-term condition that affects Americans. Most
individuals who lose their hearing do so through recreational activities, which results to hearing
loss complaints in the workplace. Cannady discusses about programs that protect the employees’
hearing and try to keep people from losing their hearing. There are approaches and strategies that
employees and organizations may benefit from having and may include, employees use of
organizational strategies and personal protective control mechanisms to lessen their exposures
during recreation. Controls could include distancing from loud places, reducing exposure time,
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keeping a safe distance from the source, turning down the volume of the source, or getting rid of
old equipment that causes unnecessary noises.
New Employee Training Article
Per their research, Kostanjek and Jagodi outlined a deployment program established by
the implementer in coordination with the Human Resources functional organization for all new
recruits until they were categorized into their functional units that assisted in their specialized job
profession training. According to Kostanjek and Jagodic (2020), deployment and specialized
training play a key role in accelerating the transfer of knowledge, enhancing the integration of
new employees into the workplace, and contributing considerably to the transfer of information
and skills.
Lead Exposure Article
The study group received 1000 ppm of lead acetate in tap water for 90 days, and revealed
that the frequency of lead exposure was around 21 mg Pb/day per animal. This was intentionally
done to create a model to analyze Pb exchange throughout bodily compartments, namely in bone
tissue. It was concluded that the lead exposure resulted in changing the underlying bone
structure.
Return on Investment Article
In the research conducted by Hoeckel et al. (2019), the authors discuss that everything had an
equal balance when all points are reviewed to understand an supplier-risk’s maturity and
performance. The return on investment is very highly dependent on the risk tolerance level of the
company and his ability to study and decipher the equilibrium between maturity and
performance of the supplier/risk management.
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Research Objectives Safety is a crucial component in an organization, especially in areas where harmful
substances are extracted. The government, employees, and the organization executive have a role
in ensuring safety. There are a bunch of perils in today’s workplaces, which, if not taken care of,
will result in employees’ physical injuries and bruises, affecting their ability to report to duty and
the organization’s overall performance. Sun Coast should implement strategies that guarantee the
health and safety of employees at work; if not, they could face serious lawsuits that could lead to
hefty fines or even closure. A lot can be done to reduce employee illnesses and injuries by
conducting a thorough risk assessment, making policies, and implementing the proper
procedures (ComplianceQuest, 2022). Health strategies are crucial in ensuring that both Sun
Coast and the employees understand the potential dangers in the workplace. Training is an
additional component that could be used in Sun Coast to enlighten the employees on the right
procedures, practices, and ways to act to reduce the risk of getting sick, hurt, or contaminated.
Finally, reimbursing for work-related illnesses and injuries affects the company’s bottom line a
lot, which is why it’s essential to have health and safety procedures in place.
However, apart from the company’s management, the government has a role in enforcing
health and safety laws requiring companies to follow specific rules when making their health and
safety procedures (OSHA, 2022). Suppose a company doesn’t ensure its employees have a safe
workplace. In that case, hefty fines should be charged, or even the organization shut down
temporarily or permanently, depending on how terrible the violations are. If the company does
not adhere to the stipulated rules for health and safety, it could lose money, employees, clients,
vendors, productivity, and the company’s brand image.
Research Problem
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Sun Coast Remediation has lost a significant amount of money in the past due to how
unsafe and dangerous its work environment is. The company’s management has observed that
employees are calling in sick more often, getting hurt or sick more often, and performing less
work. So, it’s important to research how to make and enforce a health and safety policy, as well
as how it impacts the company’s employees and how well they do their jobs.
Research Objective
• To examine the correlation between health and safety at work and also how productive
employees are.
• To figure out what employers can do to improve health and safety at work.
• To find out what employees can do to improve health and safety at work.
• To propose a safety and health policy for Sun Coast Remediation that is complete and
efficient.
Research Questions and Hypotheses RQ1: What is the correlation between health and safety conditions at work and how productive
workers are?
Ho: There exists no statistically significant connection between health and safety conditions at
work (independent) and how productive workers are (dependent).
H1: There is a statistically significant relation between health and safety conditions at work and
how much work people get done.
RQ2: What do employers do to enhance the health and safety of their employees at work?
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Ho: Employers do not even make a big difference when it comes to enhancing health and safety
at work.
H1: Employers have a significant role to play in improving health and safety at work.
RQ3: What do staff do to improve health and safety at work?
Ho: Employees don’t make a big difference when it comes to improving health and safety at
work.
H1: Employees have a significant role to play in making the workplace more secure and
healthier.
Research Methodology, Design, and Methods
Sun Coast has a number of issues that must be fixed, ranging from These include
particulate matter, how well safety training is done, exposure to noise, training for new
employees, exposure to lead, and ROI. To address these issues properly, it’s important to use
good research methods and assess the issues at stake to safeguard the health and safety of the
employees and the organization as a whole.
Research Methodology
In this investigation, quantitative methods will be used to do the research. Quantitative
methods operate well when the documents are in the form of numbers, which allows to quantify
the correlation between variables so that data can be evaluated (Creswell & Creswell, 2018).
Quantitative research is the most appropriate method to compare Sun Coast because it gives
more social and process-based data that won’t alter the findings for Sun Coast.
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Research Design
The type of research employed is descriptive research, which does not incorporate
experiments. It will help researchers reach to end state by applying statistical analysis to test
formal hypothesis, thus by exposing the information. This research plan will help Sun Coast
take care of the issues.
Research Methods
Sun Coast will employ three different kinds of research to examine the data that is
collected for this study: descriptive statistics, causal-comparative research, and correlational
research. Considerations about the quantity of lead in the blood are examined using descriptive
statistics. Blood is tested using descriptive statistical techniques to determine how working in
places with lead affects people. The results are based on data and numbers. Correlation methods
are used to discover how the different factors in particulate matter and return on investment are
related to each other. The correlational research method is employed to describe and measure
how closely two or more variables or sets of scores are related to each other. Causal-
comparative research is used to research things like levels of noise, how well safety training
works, and how to train new employees.
Data Collection Methods
The data in this study is retrieved came from questionnaires administered, methods of
observation, and the documentation analysis. This technique also saves money since the
research is done in the same area. The observation methods used to quantify the variables can’t
be garnered from the employees at work. That’s why it’s important, because the researcher gets
first-hand information from the observation method.
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Sampling Design
Simple random and convenience sampling is an general principle applied in sampling
design. The actual results of an experiment will be generated from random samples, which
cannot be changed. This will be done to make sure that there are no biases and that the sampling
method would get the right number of samples. The data will be collected through observation,
which will be used as the sampling method. Convenience samples seem to be used in places
where employees have been subjected and need to be examined, so the sample cannot be selected
at random.
Data Analysis Procedures
RQ1 attempts to find out if there is a strong correlation between the particulate matter and the
health conditions of employees. Correlation is the best method for evaluating the RQ1
hypotheses because I need to know if there is a relationship.
RQ2 attempts to find out if there is a strong link between the sum of money spent on safety
training and the number of hours lost due to accidents. Regression is the best way to test the RQ2
hypotheses since what matters is whether or not there a relationship. Regression will show if the
health and safety training cut down on lost-time hours and if we can predict lost-time hours
based on how much we spent on training.
RQ3 attempts to find out if we can use noise level to figure out what kind of ear protection is
best. Regression is the best way to test the RQ3 hypotheses because I need to know if there is a
relationship. Regression will demonstrate if there is a link between the use of earplugs, the
amount of noise, and hearing loss.
RQ4 is used to find out if the different training program has worked better than the old one. The t
test is the best way to find out if there is a statistically significant difference between Group A
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and Group B employees.
RQ5 is used to identify out if lead exposure changed the amount of lead in the blood
significantly. The t test will show whether the new training program is better than the old one.
The t test is the best way to test the RQ5 hypotheses because I need to know if there is a
statistical difference between how employees were before and after they were exposed to lead.
The t test will show if the exposure caused the employees’ blood lead levels to rise.
RQ6 is used to discover out if the four lines of service all have the same average return on
investment. Since more than one line of service is of interest, ANOVA is the best way to test the
RQ6 hypothesis.
Data Analysis: Descriptive Statistics and Assumption Testing Correlation: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
Microns Frequency
0.1-1.0 8
1.1-2.0 7
2.1-3.0 7
3.1-4.0 10
4.1-5.0 13
6.1-7.0 9
7.1-8.0 15
8.1-9.0 18
9.1-10.0 10
10.1-11.0 6
More 0
Mean Sick Days Frequency
1.0-2.0 1
3.0-4.0 6
5.0-6.0 31
7.0-8-0 42
9.0-10.0 19
11.0-12.0 4
15
More 0
Histogram
Descriptive Statistics Table
Microns
Mean sick days per employee
(annual)
Mean 5.6573 Mean 7.1262
Standard Error 0.2556 Standard Error 0.1865
Median 6 Median 7
Mode 8 Mode 7
Standard Deviation 2.5941 Standard Deviation 1.8926
Sample Variance 6.7291 Sample Variance 3.5820
Kurtosis -0.8522 Kurtosis 0.1249
Skewness -0.3733 Skewness 0.1422
Range 9.8 Range 10
Minimum 0.2 Minimum 2
Maximum 10 Maximum 12
0
10
20
F re
q u
en cy
Microns
Histogram for Microns
0
10
20
30
40
50
F re
q u
en cy
Mean Sick Days
Histogram for mean annual sick
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Sum 582.7 Sum 734
Count 103 Count 103
Measurement Scale
The measurements of scale used here include interval scale for the Microns and nominal for the
mean sick days of the employees. The two variable are continuous.
Measure of Central Tendency
Mean, mode and median were used to measure the central tendency. The mean, mode, and median
of the microns are 5.66, 8, and 6 respectively. The mean, mode, and median of the mean sick days
are 7.1262, 7, and 7 respectively. The median, mean, and mode are used to determine whether data
has a normal distributions.
Skewness and Kurtosis
The kurtosis and skewness of the microns are -0.8522 and -0.3733 respectively. The kurtosis and
skewness for the mean sick days per annum are 0.1249 and 0.1422 respectively. Skewness should
range from -3 to 3 while kurtosis should range from -10 to 10. The skewness of microns does not
fall within the acceptable range while that of the employee’s mean annual sick days fall within the
acceptable ranges. The kurtosis for both variables fall within the acceptable range.
Evaluation
From the descriptive statistics, the values for the different central tendency measures including
median, mean, and mode were significantly different indicating that the microns data is not
normally distributed. The values of central tendency measures for the employee’s mean annual
sick days were approximately equal which shows that its normally distributed. The skewness of
microns does not fall within the acceptable range while both the kurtosis and skewness of the
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employee’s mean annual sick days fall within the acceptable ranges. From the descriptive
statistics, it is clear that data on microns is not normally distributed while the data on the mean
sick days for an employee is normally distributed.
The assumptions for correlation were not met.
Simple Regression: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
Lost time Frequency
1.-50 6
51-100 26
101-150 44
151-200 54
201-250 55
251-300 30
301-350 7
351-400 1
More 0
Histogram
Descriptive Statistics Table
0
10
20
30
40
50
60
1.-50 51-100 101-150 151-200 201-250 251-300 301-350 351-400 More
F re
q u
en cy
lost time hours
Histogram for lost time hours
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safety training
expenditure lost time hours
Mean 595.9844 Mean 188.0045
Standard Error 31.4770 Standard Error 4.8031
Median 507.772 Median 190
Mode 234 Mode 190
Standard Deviation 470.0520 Standard Deviation 71.7254
Sample Variance 220948.8463 Sample Variance 5144.5360
Kurtosis 0.4441 Kurtosis -0.5012
Skewness 0.95133 Skewness -0.0820
Range 2251.404 Range 350
Minimum 20.456 Minimum 10
Maximum 2271.86 Maximum 360
Sum 132904.517 Sum 41925
Count 223 Count 223
Measurement Scale
The dependent variable (lost time hours) was measured using an interval scale
Measure of Central Tendency
Median, mean, and mode are used as central tendency measures. The median, mean, and mode of
the safety training expenditure are 507.772, 595.9844, and 234 respectively. The median, mean,
and mode of the lost time hours are 190, 188.0045, and 190 respectively. Median, mean, and mode
are used to show whether data has a normal distributions.
Skewness and Kurtosis
The skewness for safety training expenditure, and lost time are 0.9513 and 0.0820
respectively. The kurtosis for safety training expenditure, and lost time hours are 0.4441 and
0.5012 respectively. Skewness should range from -3 to 3 while kurtosis should range from -10 to
10. The skewness and kurtosis for both variables fall within the acceptable range.
Evaluation
19
From the descriptive statistics, the values for the different central tendency measures
including median, mean, and mode were significantly equal for the dependent variable (lost
time) indicating that the data is approximately normally distributed.
The assumptions for simple regression analysis were met.
Multiple Regression: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
Decibel Frequency
100.001-110.000 36
110.001-120.000 324
120.001-130.000 768
130.001-140.000 373
140.001-150.000 2
More 0
Histogram
Descriptive Statistics Table
Frequency (Hz) Angle Chord Length
Mean 2886.3806 Mean 6.7823 Mean 0.1161
Standard Error 81.3178 Standard Error 0.1527 Standard Error 0.0013
0 200 400 600 800
1000
F re
q u
en cy
Decibel
Histogram for Decibel
20
Median 1600 Median 5.4 Median 0.1176
Mode 2000 Mode 0 Mode 0.0917
Standard
Deviation 3152.5731
Standard
Deviation 5.9181
Standard
Deviation 0.0487
Sample
Variance 9938717.3837
Sample
Variance 35.0242
Sample
Variance 0.0024
Kurtosis 5.7087 Kurtosis -0.4130 Kurtosis -1.1782
Skewness 2.1371 Skewness 0.6892 Skewness -0.0275
Range 19800 Range 22.2 Range 0.1697
Minimum 200 Minimum 0 Minimum 0.03
Maximum 20000 Maximum 22.2 Maximum 0.1997
Sum 4338230 Sum 10193.8 Sum 174.5585
Count 1503 Count 1503 Count 1503
Velocity Displacement Decibel
Mean 50.8607 Mean 0.0111 Mean 124.8359
Standard Error 0.4017 Standard Error 0.0003 Standard Error 0.1779
Median 39.6 Median 0.00495741 Median 125.721
Mode 39.6 Mode 0.00529514 Mode 127.315
Standard
Deviation 15.5728
Standard
Deviation 0.0132
Standard
Deviation 6.8987
Sample
Variance 242.5116
Sample
Variance 0.0002
Sample
Variance 47.5915
Kurtosis -1.5640 Kurtosis 2.2189 Kurtosis -0.3142
Skewness 0.2359 Skewness 1.7022 Skewness -0.4190
Range 39.6 Range 0.058010618 Range 37.607
Minimum 31.7 Minimum 0.000400682 Minimum 103.38
Maximum 71.3 Maximum 0.0584113 Maximum 140.987
Sum 76443.7 Sum 16.74324023 Sum 187628.422
Count 1503 Count 1503 Count 1503
Measurement Scale
The dependent variable was measured on an interval scale.
Measure of Central Tendency
21
Median, mean, and mode were used to measure central tendency. The median, mean, and mode
of Decibel are 125.721, 124.8359,and 127.315. The median, mean, and mode are used to
determine whether the data has a normal distributions.
Skewness and Kurtosis
Skewness should range from -3 to 3 while kurtosis should range from -10 to 10. The
skewness and kurtosis of Decibel are -0.4190 and -0.3142 respectively The skewness and kurtosis
the dependent variable fall within the acceptable range.
Evaluation
From the descriptive statistics, the values for median, mean, and mode of the dependent
variable were approximately equal. The skewness and kurtosis of the dependent variable fall
within the acceptable range hence indicating that the data was normally distributed.
The assumptions for multiple regression analysis were met.
Independent Samples t Test: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
Group A Frequency
41-50 4
51-60 8
61-70 20
71-80 21
81-90 8
91-100 1
More 0
Group B Frequency
71-75 2
76-80 12
81-85 21
86-90 19
22
91-95 6
96-100 2
More 0
Histogram
Descriptive Statistics Table
Group A Group B
Mean 69.7903 Mean 84.7742
Standard Error 1.4028 Standard Error 0.6595
Median 70 Median 85
Mode 80 Mode 85
Standard Deviation 11.0456 Standard Deviation 5.1927
Sample Variance 122.0045 Sample Variance 26.9646
Kurtosis -0.7767 Kurtosis -0.3525
0
10
20
30
71-75 76-80 81-85 86-90 91-95 96-100 More
F re
q u
en cy
Group B
Histogram for Group B
0
10
20
30
41-50 51-60 61-70 71-80 81-90 91-100 More
F re
q u
en cy
Group A
Histogram for Group A
23
Skewness -0.0868 Skewness 0.1441
Range 41 Range 22
Minimum 50 Minimum 75
Maximum 91 Maximum 97
Sum 4327 Sum 5256
Count 62 Count 62
Measurement Scale
Both variables were measured on an ordinal scale.
Measure of Central Tendency
Median, mean, and mode were used to measure central tendency. The median, mean, and mode
for Group A are 80, 69.7903, and 70 respectively. The median, mean, and mode for Group B are
85, 84.7742, and 85 respectively. The median, mean, and mode are used to determine whether the
data has a normal distributions.
Skewness and Kurtosis
Skewness should range from -3 to 3 while kurtosis should range from -10 to 10. The
kurtosis for Group A and Group B are -0.7797 and -0.3525 respectively. The skewness for Group
A and Group B are -0.0868 and 0.1441 respectively. The kurtosis and skewness for both groups
fall within the acceptable range.
Evaluation
From the descriptive statistics, the values for the different measures of central tendency
including mean, mode, and median were approximately equal for the both groups. The skewness
and kurtosis for both groups fall within the acceptable range hence indicating that the data was
normally distributed.
The assumptions for independent t Test analysis were met.
24
Dependent Samples (Paired-Samples) t Test: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
Pre-Exposure Frequency
1.0- 10.0 3
11.0-20.0 6
21.0-30.0 9
31.0-40.0 15
41.0-50.0 15
51.0-60.0 1
More 0
Post-Exposure Frequency
1.0- 10.0 3
11.0-20.0 6
21.0-30.0 9
31.0-40.0 13
41.0-50.0 17
51.0-60.0 1
More 0
Histogram
0
5
10
15
20
1.0- 10.0 11.0-20.021.0-30.031.0-40.041.0-50.051.0-60.0 More
F re
q u
en cy
Pre-Exposure
Histogram for Pre-Exposure
25
Descriptive Statistics Table
Pre-Exposure Post-Exposure
Mean 32.8571 Mean 33.2857
Standard Error 1.7523 Standard Error 1.7814
Median 35 Median 36
Mode 36 Mode 38
Standard Deviation 12.2661 Standard Deviation 12.4700
Sample Variance 150.4583 Sample Variance 155.5000
Kurtosis -0.5760 Kurtosis -0.6542
Skewness -0.4251 Skewness -0.4836
Range 50 Range 50
Minimum 6 Minimum 6
Maximum 56 Maximum 56
Sum 1610 Sum 1631
Count 49 Count 49
Measurement Scale
Both variables were measured on an ordinal scale.
Measure of Central Tendency
0
5
10
15
20
1.0- 10.0 11.0-20.0 21.0-30.0 31.0-40.0 41.0-50.0 51.0-60.0 More
F re
q u
en cy
Post-Exposure
Histogram for Post-Exposure
26
Median, mean, and mode were used to measure central tendency. The median, mean, and mode
for both groups are approximately equal. The Median, mean, and mode are used to determine
whether the data has a normal distributions.
Skewness and Kurtosis
Skewness should range from -3 to 3 while kurtosis should range from -10 to 10.
The kurtosis and skewness for both groups fall within the acceptable range.
Evaluation
From the descriptive statistics, the values for the median, mean, and mode were
approximately equal for both groups. The skewness and kurtosis for both groups fall within the
acceptable range hence indicating that the data was normally distributed.
The assumptions for Dependent Samples t test were met.
ANOVA: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
Air Frequency
1.0-3.0 1
4.0-6.0 4
7.0-9.0 6
10.0-12.0 7
13.0-15.0 2
More 0
B = Soil Frequency
4.0-6.0 1
7.0-9.0 12
10.0-12.0 6
13.0-15.0 1
More 0
27
C = Water Frequency
1.0-3.0 1
4.0-6.0 10
7.0-9.0 5
10.0-12.0 4
More 0
D = Training Frequency
2.0-3.0 1
4.0-5.0 10
6.0-7.0 8
8.0-9.0 1
More 0
Histogram
0
2
4
6
8
1.0-3.0 4.0-6.0 7.0-9.0 10.0-12.013.0-15.0 More
F re
q u
en cy
Air
Histogram for B = Air
0
5
10
15
4.0-6.0 7.0-9.0 10.0-12.0 13.0-15.0 More
F re
q u
en cy
B = Soil
Histogram for B = Soil
28
Descriptive Statistics Table
Air Soil Water 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 0.27
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 1.19
Sample
Variance 9.36
Sample
Variance 3.04
Sample
Variance 6.63
Sample
Variance 1.41
Kurtosis
–
0.63 Kurtosis 0.12 Kurtosis
–
0.24 Kurtosis 0.25
Skewness
–
0.36 Skewness 0.49 Skewness 0.76 Skewness 0.16
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
0
5
10
15
1.0-3.0 4.0-6.0 7.0-9.0 10.0-12.0 More F
re q
u en
cy
C = Water
Histogram for C = Water
0
5
10
15
2.0-3.0 4.0-5.0 6.0-7.0 8.0-9.0 More
F re
q u
en cy
D = Training
Histogram for D = Training
29
Count 20 Count 20 Count 20 Count 20
Measurement Scale
All variables were measured on a ratio scale.
Measure of Central Tendency
Median, mean, and mode were used to measure central tendency. The median, mean, and mode
for the four variables were significantly different. The median, mean, and mode are used to
determine whether the data has a normal distributions.
Skewness and Kurtosis
Skewness should range from -3 to 3 while kurtosis should range from -10 to 10.
The kurtosis and skewness for the four variables fall within the acceptable range.
Evaluation
From the descriptive statistics, the values for median, mean, and mode were
significantly different for all factors. The skewness and kurtosis for all the factors fall within the
acceptable range hence indicating that the data was normally distributed. The assumptions for
ANOVA were met.
Data Analysis: Hypothesis Testing Correlation: Hypothesis Testing
Formulating Hypothesis
Ho: Employers do not even make a big difference when it comes to enhancing health and safety
at work.
H1: Employers have a significant role to play in improving health and safety at work.
30
Excel Output
SUMMARY OUTPUT
microns
mean
annual sick
days per
employee
microns 1 mean
annual sick
days per
employee
–
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 106.2362 1.89059E-17 Residual 101 178.0638994 1.763009
Total 102 365.3592233
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Intercept 10.08144483 0.315156969 31.98865 0.000 9.456258184 10.70663
microns
–
0.522376554 0.050681267 -10.3071 1.89E-17
–
0.622914554 -0.42184
Pearson correlation coefficient f r = -0.71598 shows that there exists a strong positive
relationship between microns and the average number of sick days an employee takes each
year. This gives us a r2 of 0.5126, which means that we can explain 51.26% of the
31
differences between the two variables. Using a level of significance of 0.05, the results show
that the p-value is 0.000 0.05. So, there are enough reasons to say that the alternative
hypothesis is more likely than the null hypothesis. There is a statistically significant link
between microns and the average number of sick days an employee takes each year.
Simple Regression: Hypothesis Testing
Ho: Employees don’t make a big difference when it comes to improving health and safety at
work.
H1: Employees have a significant role to play in making the workplace more secure and
healthier.
SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.939559
R Square 0.882772
Adjusted R Square 0.882241
Standard Error 24.61329
Observations 223
ANOVA
df SS MS F
Significanc
e F
Regression 1 1008202 1008202
1664.21
1 7.7E-105 Residual 221 133884.9 605.814
Total 222 1142087
Coefficient
s
Standard
Error t Stat P-value Lower 95%
Upper
95%
Intercept 273.4494 2.665262
102.597
6
2.1E-
188 268.1968
278.70
2
safety training
expenditure -0.14337 0.003514
–
40.7947
7.7E-
105 -0.15029
–
0.1364
4
32
The Pearson coefficient of correlation, which is equal to the multiple r, is 0.939. This
demonstrated that the correlation between a set of variables is very strong. This gives an r-
squared of 0.8828, which means that the regression model can explain 88.28% of the changes in
the dependent variables (Draper & Smith, 2014). Using an alpha of 0.05, the ANOVA F value is
1664.21 and the p-value is 0.000 0.05. This is strong evidence that the null hypothesis should be
thrown out. So, the slope of regression is a long way from being equal to zero. The equation for
regression is Y = -0.1434X + 273.449. Because of this, the cost of safety training goes down by
0.1434 units for every hour of lost time. The level of significance for the p-value of the
regression coefficient is 7.7E-105 0.05. The regression coefficient is statistically important
because of this.
Multiple Regression: Hypothesis Testing
Ho: There exists no statistically significant connection between health and safety conditions at
work (independent) and how productive workers are (dependent).
H1: There is a statistically significant relation between health and safety conditions at work and
how much work people get done.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.601842
R Square 0.362214
Adjusted R Square 0.360083
Standard Error 5.518566
Observations 1503
ANOVA
df SS MS F
Significan
ce F Regression 5 25891.89 5178.37 170.036 2.1E-143
33
8 1
Residual 1497 45590.49
30.4545
7
Total 1502 71482.38
Coefficien
ts
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Intercept 126.8225 0.62382
203.299
7 0 125.5988
128.046
1
Frequency (Hz) -0.00112 4.76E-05
–
23.4885
4.1E-
104 -0.00121
–
0.00102
Angle in Degrees 0.047342 0.037308
1.26895
7
0.20465
4 -0.02584
0.12052
4
Chord Length -5.49532 2.927962
–
1.87684
0.06073
4 -11.2387
0.24802
6
Velocity (Meters per
Second) 0.08324 0.0093
8.95031
7
1.02E-
18 0.064997
0.10148
2
Displacement -240.506 16.51903
–
14.5593
5.21E-
45 -272.909
–
208.103
The Pearson coefficient of correlation, which is equal to the multiple r, is 0.939. This
means that the relationship between the two variables is very strong. This gives an r-squared of
0.8828, which means that the regression model can explain 88.28% of the changes in the
dependent variables (Brook & Arnold, 2018). Using an alpha of 0.05, the ANOVA F value is
1664.21 and the p-value is 0.000 0.05. This is strong evidence that the null hypothesis should be
thrown out. So, the slope of regression is a long way from being equal to zero. The equation for
regression is Y = -0.1434X + 273.449. Because of this, the cost of safety training goes down by
0.1434 units for every hour of lost time. The level of significance for the p-value of the
regression coefficient is 7.7E-105 0.05. The regression coefficient is statistically important
because of this.
Independent Samples t Test: Hypothesis Testing
H04: The mean score of both populations is same.
34
Ha4: There is a discrepancy in average scores.
Excel output for the independent sample t-test
A and B are similar.
t-Test: Assume the Variances is Unequal
Training Scores of Group A Prior Group B Revised Training
Scores
The Mean 69.7903 84.7742
The Variance 122.005 27.0000
Outcome 62.0000 62.0000
df 87 t Stat -9.6667 P(T<=t) 9.7E-16 (t Critical) 1.6626 P(T<=t) 1.94E-15 (t Critical) 1.9876
The statistics of t (-9.667), when contrasted to the statistics of t-critical and t-statics
trails, equal -1.9876. The t (87) = -9.667t-critical = -1.9876 Ho rejection failure, so group A and
B are comparable.
Dependent Samples (Paired Samples) t Test: Hypothesis Testing
The data provided for analysis consists of mg/dL pre-exposure and g/dL post-exposure
values for an employee of a certain organization. Our primary problem is to determine whether
the means of the two linked samples are equal. The hypothesis employed is as follows:
H05: Whenever the two associated means are equal, H0: 1 = 2 holds.
Ha5: H1: 1 > 2 if the two associated means are not identical.
t-Test: Paired Means
μg/dL Pre-Exposure μg/dL Post-Exposure
Mean 32.8600 33.3000
Variance 150.4583 155.5
35
Outcome 49.0000 49.0000
Pearson Correlation 0.9900 df 48 t Stat -1.9000 P(T<=t) 0.02978 (t Critical) 1.6772 [P(T<=t)] 0.0596 (t Critical) 2.0106
Given the hypothesis is a two-tailed hypothesis, we use two-tailed results.
The t-statistic = -1.93
Compared t-statics to t-critical with two tails = -2,011.
Since t (48) = -1,930 > t-critical = -2,011; reject H0 and infer that the means of the two linked
groups (Pre-Exposure mg/dL and Post-Exposure g/dL) are not equal or are different.
ANOVA: Hypothesis Testing
Water, air, training data, and soil are included in the investment return project’s data analysis.
The examination of equality between the two samples will be detected. The tested hypothesis
was as follows: (a=0.05)
HO6: μ1 = μ2 = μ3 = μ4
Ha6: The means are not the same
The F-statistics is 11,923, whereas F-critical is 2,7249.
Since F-statistics = 11.923 > F-critical = 2.7249, reject H0 and infer that the means for air, soil,
water, and training ROI (%) are not all equal.
Single Factor
SUMMARY
Groups Variance Count Average Sum
36
Air 9.3600 20.0000 8.9000 178.0000
Soil 3.0400 20.0000 9.1000 182.0000
Water 6.6316 20.0000 7.0000 140.0000
Training 1.4105 20.0000 5.4000 108.0000
ANOVA
Variation Source F SS MS df F crit Value of P
For Groups 11.9200 182.8000 60.9300 3 2.7200 1.7600E-06
Within the Groups 388.4000 5.1000 76
Total 571.2 79
Findings Students should discuss the findings in the context of Sun Coast’s problems and the
associated research objectives and questions. Restate each research objective and discuss them in
the context of your hypothesis testing results. The following are some things to consider. What
answers did the analysis provide to your research questions? What do those answers tell you?
What are the implications of those answers? Use the following subheadings to include all
required information. Delete instructions highlighted in yellow before submitting this
assignment.
Example:
RO1: Determine if a person’s height is related to weight.
The results of the statistical testing showed that a person’s height is related to their
weight. It is a relatively strong and positive relationship between height and weight. We would,
therefore, expect to see in our population taller people having a greater weight relative to those
of shorter people. This determination suggests restrictions on industrial equipment should be
stated in maximum pounds allowed rather than maximum number of people allowed.
37
RO2:
RO3:
RO4:
RO5:
RO6:
Recommendations Students should include recommendations here in paragraph form. This section
should be your professional thoughts based upon the findings from the hypothesis testing. You
are the researcher and Sun Coast’s leadership team is relying on you to make evidence-based
recommendations. Use the following subheadings to include all required information. Delete
instructions highlighted in yellow before submitting this assignment.
Particulate Matter Recommendation
Safety Training Effectiveness Recommendation
Sound-Level Exposure Recommendation
New Employee Training Recommendation
Lead Exposure Recommendation
Return on Investment Recommendation
38
References
Hoeckel, C. A., Neuert, J., Schüller, M., Schwamborn, A., & Wang, J. (2019). Return on
Investment from Supplier/Risk Management. Journal of Business & Management, 25(2).
Kostanjšek, K., & JAGODIĆ, G. (2020). Employee Training and Education. Management
(18544223), 15(1).
McClellan, R. O. (2016). Providing context for ambient particulate matter and estimates of
attributable mortality. Risk Analysis, 36(9), 1755-1765.
Norris, M. W., Spicer, K., & Byrd, T. (2019). Virtual reality: the new pathway for effective
safety training. Professional Safety, 64(06), 36-39.
ComplianceQuest. (2022, September 21). What is Employee Safety and its importance &
responsibilities? ComplianceQuest QHSE Solutions. Retrieved September 26, 2022, from
Employer Responsibilities | Occupational Safety and Health Administration (OSHA). (2022).
Retrieved September 26, 2022, from https://www.osha.gov/workers/employer-
responsibilities
Cooksey, R. W. (2020). Descriptive statistics for summarising data. In Illustrating statistical
procedures: Finding meaning in quantitative data (pp. 61-139). Springer, Singapore.
Peeples, J., Xu, W., & Zare, A. (2021). Histogram layers for texture analysis. IEEE Transactions
on Artificial Intelligence.
Hu, J., Chen, Y., Leng, C., & Tang, C. Y. (2021). Regression Analysis of Correlations for
Correlated Data. arXiv preprint arXiv:2109.05861.
39
Kim, T. K., & Park, J. H. (2019). More about the basic assumptions of t-test: normality and
sample size. Korean journal of anesthesiology, 72(4), 331-335.
Chen, S. X., Li, J., & Zhong, P. S. (2019). Two-sample and ANOVA tests for high dimensional
means. The Annals of Statistics, 47(3), 1443-1474.
Brook, R. J., & Arnold, G. C. (2018). Applied regression analysis and experimental design. CRC
Press
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