Tag: leadership

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

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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

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Microns

Histogram for Microns

0

10

20

30

40

50

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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

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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

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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

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Decibel

Histogram for Decibel

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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

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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

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Group B

Histogram for Group B

0

10

20

30

41-50 51-60 61-70 71-80 81-90 91-100 More

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Group A

Histogram for Group A

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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.

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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

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Pre-Exposure

Histogram for Pre-Exposure

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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

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Post-Exposure

Histogram for Post-Exposure

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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

Employee Safety

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.

Employee Safety

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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