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Business executives are primarily concerned about the strategies to adopt to increase business transactions. Therefore, they screen various aspects to determine the critical areas that require to be solved using the business research method. The senior leaders at Sun Coast want to see the projects conducted to completion.

The paper comprises sections such as data collection, generating statements of problems, literature review, research objectives, research questions and hypothesis, methodology, design and methods, data analysis, finding, and recommendation.

Senior leadership at Sun Coast has identified several areas for concern that they believe could be solved using business research methods. The previous director was tasked with researching to help provide information to make decisions about these issues. Although data were collected, the project was never completed. Senior leadership is interested in seeing the project through to fruition. The following is the completion of that project and includes a statement of the problems, literature review, research objectives, research questions and hypotheses, research methodology, design and methods, data analysis, findings, and recommendations.

Six business problems were identified:

There is a concern that job-site particle pollution is adversely impacting employee health. Although respirators are required in certain environments, particulate matter (PM) varies in size depending on the project and job site. PM between 10 and 2.5 microns can float in the air for minutes to hours (e.g., asbestos, mold spores, pollen, cement dust, fly ash), while PM less than 2.5 microns can float in the air for hours to weeks (e.g., bacteria, viruses, oil smoke, smog, soot). Due to PM's smaller size, less than 2.5 microns, is potentially more harmful than PM between 10 and 2.5 since the conditions are more suitable for inhalation. PM less than 2.5 can also be inhaled into the deeper regions of the lungs, potentially causing more deleterious health effects. It would be helpful to understand if there is a relationship between PM size and employee health. PM air quality data have been collected from 103 job sites, which are recorded in microns. Data are also available for average annual sick days per employee per job site.

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.

Sun Coast’s contracts generally involve work in noisy environments due to a variety of heavy equipment being used for both remediation and the clients’ ongoing operations on the job sites. Standard earplugs are adequate to protect employee hearing if the decibel levels are less than 120 decibels (dB). More advanced and expensive hearing protection is required for environments with noise levels exceeding 120 dB, such as earmuffs. Historical data have been collected from 1,503 contracts for several variables that are believed to contribute to excessive dB levels. It would be important if these data could be used to predict the dB levels of work environments before placing employees on-site for future contracts. This would help the safety department plan for the procurement of appropriate ear protection for employees.

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.

Employees working on job sites to remediate lead must be monitored. Lead levels in the blood are measured as micrograms of lead per deciliter of blood (μg/dL). A baseline blood test is taken pre-exposure and post-exposure at the conclusion of the remediation. Data are available for 49 employees who recently concluded a two-year-long lead remediation project. It is necessary to determine if blood lead levels have increased.

Sun Coast offers four service lines to their customers, including air monitoring, soil remediation, water reclamation, and health and safety training. Sun Coast would like to know if each line of service offers the same return-on-investment. Return-on-investment data are available for air monitoring, soil remediation, water reclamation, and health and safety training projects. If return-on-investment is not the same for all service lines, it would be helpful to know where differences exist.

Vinnikov, D., Tulekov, Z., & (Raushanova, A. (2020). Occupational exposure to participate matter from air pollution in the outdoor workplaces in Almaty during the cold season. PloS one, 15(1).

The article authors are Al-Farabi Kazakh National University, School of Public Health, Almaty, Kazakhstan, National Research Tomsk State University; hence they qualify to write it. Vinnikov et al. (2020), the primary purpose was to study the occupational particulate matter's level in outdoor work settings during the cold season. The study used AVOVA in data analysis. Despite the research in Almaty, the same urban landscape gives a similar concept regarding the association between increases of particulate matter in the cold season. The researchers established that M10 TWA lay between 0.050 to 2.075 mg/m3 with 0.366 as geometric mean and median 0.352 mg/m3, implying a high level of particulate matter. I believe that the research will help implement ways to prevent pollutants at work based on the research's evidence-based findings.

Hill III, J.J., Slade, M.D., Cantley, L., Vegso, S., Fiellin, M., & Cullen, M.R. (2008). The relationships between lost work time and duration of absence spell a proposal for a payroll-driven absenteeism measure. Journal of occupational and environmental medicine/American College of Occupation and Environmental Medicine, 50(7), 840.

The above article, its authors are affiliates of recognized institutions of higher learning such as Yale University. The study's purpose was to establish critical metrics for use in determining the lost work time and duration of absences in work resulting from training. The research utilized ANOVA in determining the relationship between the work lost rate and expenditures within a healthcare context. The findings showed that hours not paid and absent days are significantly correlated with the work loss rate. The research and Sun Coast aim at establishing whether safety training can help reduce absenteeism resulting from workplace injuries. The research made a positive organizational impact in organizations can rely on workforce databases to study the absenteeism patterns and the leading cause and if these causing factors get attributed to lack of training.

Hutauruk, M. R., & Ghozali, I. (2020). Overview of return on investment on cigarette companies registered in Indonesia stock exchange. International Journal of Scientific and Technology Research.

Authors are affiliates of recognized universities such as the University of New York and the University of Liverpool. The research purpose was to justify the effect of investment returns in profitability on stock prices. The researchers used online data in IDX for data collection. The findings showed that return on equity ROE has a positive and crucial impact on stock prices. The general results were that return on equity, asset, and earning per share significantly affect stock price movements. The existing relationship between the article and Sun Coast is that the two aim at determining the viability of the projects to invest in. I believe that this research will help Sun Coast to rely on ROA, ROE, and EPS as the best investment performance measurement techniques for determining the key behaviors of market players.

The first project's objective is to determine the variation of respiratory complications during pre-exposure and post-exposure at the end of the remediation program. This objective helps understand the exposure that presents more respiratory risks than the other. The second aim is to establish if employees' absenteeism is attributed to injuries resulting from inadequate training. This second objective explores how insufficient or ineffective training increases injury rates of incidents, which contributes to workforce absenteeism (Gianino et al., 2019). The third objective is to establish whether standard earplugs are adequate to protect employees' ears if the decibel levels are less than 120 decibels. It helps in knowing the standard decibels for maintaining a healthy eardrum at the workplace.

The fourth objective is to establish whether the new training program is more effective than the earlier training intervention. It enhances the comparison between the two pieces of training to select the best one to implement in improving health and safety at the workplace. The fifth objective is to explore the variation of respiratory complications during pre-exposure and post-exposure at the end of the remediation program. Through this objective, the organization knows the exposure leads to more severe complications than the other. The final aim is to establish the existing differences in return on investment for all lines of service. It helps determine the current gap of return on investment to make a good investment decision.

The other objective is to investigate the levels of respiratory complications before and after remediation program exposure; this will help identify the impact of the remediation program on employees' respiratory complications incidences. The last goal is establishing the effect of lost-time hours on the general organizational performance. This goal with help understand how lost time hours through sick leaves affect the organization's revenue and profits.

Good research questions and hypotheses are developed from identifying gaps and developing new ideas to fill the gaps (Cai et al., 2019). Additionally, research questions must build on the existing literature by recognizing its assumptions. Research questions progress from the known facts to the unknown statement that requires validation (Francis et al., 2017). Similarly, the presented research questions and hypotheses evaluate facts and the unknown factors to establish solutions.

RQ1: Is there a relationship between particulate matter size and employee sick days?

Ho1: There is no statistically significant connection between particulate matter size and employee health.

Ha1: The alternative hypothesis is the direct opposite of the null hypothesis.

RO2: Predict lost-time hours from training expenditures

RQ2: Is there a relationship between safety training expenditure and lost-time hours?

Ho2: There is no statistically significant relationship between safety training expenditure and lost-time hours.

Ha2: The alternative hypothesis is the direct opposite of the null hypothesis.

RO3: Predict the dB level of work environments.

RQ3: Is there a relationship between frequency, angle in degrees, chord length, velocity, and displacement, and decibel level?

Ho3: There is no statistically significant relationship between frequency, angle in degrees, chord length, velocity, and displacement, and decibel level.

Ha3: The alternative hypothesis is the direct opposite of the null hypothesis.

RO4: Determine if the revised training program is more effective than the prior training program.

RQ4: Is the revised new employee training program more effective than the prior training program?

Ho4: There is no statistically significant difference in mean scores between prior training and revised training.

Ha4: There are statistical differences in the effectiveness of training for employees' groups.

RO5: Determine if employee blood lead levels have increased.

RQ5: Have employee blood lead levels increased from their pre-exposure baseline measurements?

Ho5: There is no statistically significant difference in employee blood lead levels between pre-exposure and post-exposure.

Ha5: The alternative hypothesis is the direct opposite of the null hypothesis.

RO6: Determine if the return on investment is the same for all Sun Coastlines of service.

RQ6: Are there differences in return on investment between air monitoring, soil remediation, water reclamation, and health and safety training?

Ho6: There are no statistically significant differences in ROI between air monitoring, soil remediation, water reclamation, and health and safety training.

Ha6: The alternative hypothesis is the direct opposite of the null hypothesis.

The selected research methodology is quantitative. Using this methodology, a researcher can use numbers and graphs to express the collected data when confirming the theories and assumptions about the research problem. Therefore, the procedure enables an in-depth understanding of the relationship between an independent and dependent variable in a population. In sum, the primary reasons for selecting quantitative over qualitative methods are that it is more scientific, objective, and control-sensitive.

For this project, the research design should be descriptive (non-experimental). This design will give the best results when testing the research hypothesis on the six identified problems. The design is helpful when describing a relationship between two or more variables, all without any interference from the researcher. For instance, in Sun Coast, the issue of employee safety has inadequate training as the causing factor for workplace injuries (effect). Therefore, this researcher will examine the relationship between training and injuries witnessed among the employees. These aspects will make the critical variables for establishing the connection.

The research methods that will be used for this project will be descriptive, correlational, and causal-comparative.

Descriptive research often involves collecting information through data review, surveys, interviews, or observation.

Correlational Research is used to test a null hypothesis stating no relationship exists between variables.

Causal-comparative research attempts to identify a cause-effect relationship between two or more groups.

The data collection methods that will be used is a survey in which contact can be made via telephone, which can include a skype call or video conference, mail-in which a questionnaire can be sent out, electronically where a survey can be sent through email, observation in which a researcher can count the number of people attending a certain event and finally document analysis which uses public records to gather information.

Sampling design is part of the research methodology, and it considers the total number of Sun Coast employees as the target finite population. Therefore, a sample will represent the whole workforce population in which the people to make the sample will be randomly selected. This random selection implies that the researcher will give each employee an equal probability of being chosen. Thus, a random sample becomes the sampling design for this study.

P1- This problem will use a correlation analysis to determine the existing association between the two variables by computing their relationship. A high correlation will imply a cause-effect relationship through this approach, while a low correlation will mean a weak connection between the variables (Tabuena & Hilario, 2021).

P2- This problem will use simple regression to determine critical factors and the ones not crucial to ignore.

P3- This problem will use multiple regression to determine if additional research is needed or when multiple X variables are included in the analysis to make a prediction about a change in a single Y variable.

P4- This problem will use the independent sample t-test, a null hypothesis stating there is no statistically significant difference between the two means.

P5- This problem will use the dependent sample t-test to determine whether the mean difference between two sets of observations is zero.

P6- This problem will use an ANOVA test that is like the t-test; however, it will determine if a null hypothesis that no statistically significant differences exist among means for three or more groups.

The main assumptions of a parametric test include normality of the distribution, where the histogram should show asymmetric bell shape. The other assumption is the homogeneity of variance and the linearity of the data.

5.1.1. Correlation: Descriptive Statistics and Assumption Testing

5.1.1.1. Frequency Distribution Table

From the figure above, the histogram obtained is a bell-shaped histogram. This shows that the data is normally distributed with a mean of approximately 7. This implies that the assumption of normality is met since the data is symmetric. However, the figure shows that more data in the datasets are skewed to the right than those to the left.

Descriptive Statistics Table

Measurement Scale

The measurement scale used in the data is ratio scale. This is because the dataset values cannot take a negative value since the smallest value is 2.

Measure of Central Tendency

The Mean Sun Coast Remediation for this data is 7.1262139, with a median of 7 and a mode of 7. The range between the maximum and the maximum value for this data is 10, with the maximum value being 12 and the minimum value being 2.

Skewness and Kurtosis

The skewness value for this data is 0.1422, and Similarly, the kurtosis value is 0.124922603. This, therefore, implies that the data is slightly skewed to the right. However, the amount of skewness in the data is minimal since the skewness and kurtosis values are both less than 0.5.

Evaluation

From the above histogram, the symmetrical shape of the histogram shows that the assumption of normality is met. Similarly, the linearity of data and the homogeneity of variance assumptions are met by the data and the analysis results provided.

5.1.2. Simple Regression: Descriptive Statistics and Assumption Testing

From the figure above, the histogram obtained is a bell-shaped histogram. This shows that the data is normally distributed with a mean of approximately 200. This implies that the assumption of normality is met since the data is symmetric. However, the figure shows that more data in the datasets lies to the right than those to the left.

Descriptive Statistics Table

Measurement Scale

The measurement scale used in the data is ratio scale. This is because the dataset values cannot take a negative value since the smallest value is 10.

Measure of Central Tendency

The mean value for this data is 188.0044843, with a median of 190 and a mode of 190. The range between the maximum and the maximum value for this data is 350, with the maximum value being 360 and the minimum value being 10.

Skewness and Kurtosis

The skewness value for this data is -0.08198487, and Similarly, the kurtosis value is -0.50122353. This, therefore, implies that the data is slightly skewed to the left since the skewness and kurtosis values both have negative signs. However, the amount of skewness in the data is very little since the skewness and kurtosis values are both between -0.5 and 0.5.

Evaluation

From the above histogram, the symmetrical shape of the histogram shows that the assumption of normality is met. Similarly, the linearity of data and the homogeneity of variance assumptions are met by the data and the analysis results provided.

5.1.3. Multiple Regressions: Descriptive Statistics and Assumption Testing

From the figure above, the histogram obtained is not bell-shaped. This shows that the data is not normally distributed with a mean of approximately 130. This implies that the assumption of normality is not met since the data is skewed to the left.

Descriptive Statistics Table

Measurement Scale

The measurement scale used in the data is ratio scale. This is because the dataset values cannot take a negative value since the smallest value is 103.38

Measure of Central Tendency

The mean value for this data is 124.8359, with a median of 125.721 and a mode of 127.315. The range between the maximum and the maximum value for this data is 37.607, with the maximum value being 140.987and the minimum value being 103.38.

Skewness and Kurtosis

The skewness value for this data is -0.41895. Similarly, the kurtosis value is -0.31419. This, therefore, implies that the data is slightly skewed to the left since the skewness and kurtosis values both have negative signs. However, the amount of skewness in the data is very little since the skewness and kurtosis values are both between -0.5 and 0.5.

Evaluation

5.1.4. Independent Samples t-Test: Descriptive Statistics and Assumption Testing

From the figure above, the histogram obtained is a bell-shaped histogram. This shows that the data is normally distributed with a mean of approximately 70. This implies that the assumption of normality is met since the data is symmetric. However, the figure shows that more data in the datasets lies to the left than those to the right.

From the figure above, the histogram obtained is a bell-shaped histogram. This shows that the data is normally distributed with a mean of approximately 86. This implies that the assumption of normality is met since the data is symmetric.

Descriptive Statistics Table

Measurement Scale

For both diagrams, the measurement scale used in the data is ratio scale. This is because the dataset values cannot take a negative value since the smallest value are 50 and 75.

Measure of Central Tendency

For the first diagram, the mean value for this data is 84.77419355, with a median of 85 and a mode of 85. The range between the maximum and the maximum value for this data is 22, with the maximum value being 97 and the minimum value being 75.

For the second diagram, the mean value for this data is 69.79032258, with a median of 70 and a mode of 80. The range between the maximum and the maximum value for this data is 41, with the maximum value being 91 and the minimum value being 50.

The measures of central tendencies are, therefore, all relevant to the data.

Skewness and Kurtosis

The skewness value for this data is -0.41895. Similarly, the kurtosis value is -0.31419. Therefore, this implies that the data is slightly skewed to the left since the skewness and kurtosis values have negative signs. However, the amount of skewness in the data is very little since the skewness and kurtosis values are both between -0.5 and 0.5.

Evaluation

For the first diagram, the mean value for this data is 84.77419355, with a median of 85 and a mode of 85. The range between the maximum and the maximum value for this data is 22, with the maximum value being 97 and the minimum value being 75.

For the second diagram, the mean value for this data is 69.79032258, with a median of 70 and a mode of 80. The range between the maximum and the maximum value for this data is 41, with the maximum value being 91 and the minimum value being 50.

The measures of central tendencies are, therefore, all relevant to the data.

The parametric test assumptions of linearity and normality were met in the data.

5.1.5. Dependent Samples (Paired-Samples) t-Test: Descriptive Statistics and Assumption

Frequency Distribution Table

Histogram

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

From the figure above, the histogram obtained is skewed to the left. This figure, therefore, shows that the data is not normally distributed with a mean of approximately 34. This implies that the assumption of normality is met since the data is not symmetric.

Descriptive Statistics Table

Pre-Exposure μg/dL
Mean	32.85714286
Standard Error	1.752306546
Median	35
Mode	36
Standard Deviation	12.26614582
Sample Variance	150.4583333
Kurtosis	-0.576037127
Skewness	-0.425109654
Range	50
Minimum	6
Maximum	56
Sum	1610
Count	49

Post-Exposure μg/dL
Mean	33.28571429
Standard Error	1.781423416
Median	36
Mode	38
Standard Deviation	12.46996391
Sample Variance	155.5
Kurtosis	-0.654212507
Skewness	-0.483629097
Range	50
Minimum	6
Maximum	56
Sum	1631
Count	49

Measurement Scale

For both diagrams, the measurement scale used in the data is ratio scale. This is because the dataset values cannot take a negative value since the smallest values are both 6.

Measure of Central Tendency

For the first diagram, the mean value for this data is 32.85714286, with a median of 35 and a mode of 36. The range between the maximum and the maximum value for this data is 50, with the maximum value being 56 and the minimum value being 6.

For the second diagram, the mean value for this data is 33.28571429, with a median of 36 and a mode of 38. The range between the maximum and the maximum value for this data is 50, with the maximum value being 56 and the minimum value being 6.

The measures of central tendencies are, therefore, all relevant to the data.

Skewness and Kurtosis

The skewness value for this data is -0.483629097. Similarly, the kurtosis value is -0.654212507. This, therefore, implies that the data is slightly skewed to the left since the skewness and kurtosis values both have negative signs. However, skewness in the data is significant since the skewness and kurtosis values are less than -0.5.

Evaluation

From the above diagrams, the skewness and kurtosis values are negative. Similarly, the above histogram figures clearly show that the datasets are skewed to the left; thus, the assumption of normality is not met. Besides, since the normality is not met, we conclude that the homogeneity assumptions have not been met either.

5.1.6. ANOVA: Descriptive Statistics and Assumption Testing

5.1.6.1. Frequency Distribution Table

From the figure above, the histogram obtained is skewed to the left. This figures, therefore, show that the data is not normally distributed with a mean of approximately 9. This implies that the assumption of normality is not met since the data is not symmetric.

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

From the figure above, the histogram obtained is approximately average. This figure, therefore, shows that the data is not normally distributed with a mean of approximately 6. This implies that the assumption of normality is met since the data is approximately symmetric.

Descriptive Statistics Table

Measurement Scale

The measurement scale used in the data obtained is the is nominal scale. This is because the data variables such as water, soil, and training cannot be categorized according to the order but rather are random labels whose ordering has no meaning.

For the tables above, the mean value for this data variable is 8.9, with a median of 9 and 11. The range between the maximum and the maximum value for this data is 11, with the total value being 14 and the minimum value being 3.

For the tables above, mean value for this data for the variable soil is 9.1 with a median of 9 and mode of 8. The range between the maximum and the maximum value for this data is 7, with the full value being 13 and the minimum value being 6.

For the tables above, the mean value for the variable water is 7 with a median of 6 and a mode of 6. The range between the maximum and the maximum value for this data is 9, with the maximum value being 12 and the minimum value being 3.

For the tables above, the mean value for variable training is 5.4 with a median of 5 and a mode of 5. The range between the maximum and the maximum value for this data is 5, with the total value being eight and the minimum value being 3.

Measure of Central Tendency

Skewness and Kurtosis

The skewness value for variable 1 is -0.36085. Similarly, the kurtosis value is -0.6283. This, therefore, implies that the data is slightly skewed to the left since the skewness and kurtosis values both have negative signs. However, the amount of skewness in the data is small since the skewness value is more significant than -0.5.

The skewness value for the variable soil is 0.492002. Similarly, the kurtosis value is 0.11923. Therefore, this implies that the data is slightly skewed to the right since the skewness and kurtosis values have positive signs. However, the amount of skewness in the data is negligible since the skewness value is less than 0.5.

The skewness value for the variable soil is 0.76020. Therefore, it implies that the data is skewed to the right since the skewness has a positive sign. However, the amount of skewness in the data is significant since the skewness value is greater than 0.5.

The skewness value for the variable training is 0.159183. Therefore, it implies that the data is skewed to the right since the skewness has a positive sign. However, the amount of skewness in the data is small since the skewness value is less than 0.5.

Evaluation

The parametric test assumption for homogeneity and normality is not met since data values are skewed to the right while some are skewed to the left. The linearity assumption is not satisfied either.

RO1: Determine how PM affects employee's heath at Sun Coast

The results of the statistical Testing showed that a person's PM is related to their employee health. It is a relatively strong and positive relationship between Particulate matter and health. We would, therefore, expect to see in our population high levels of particulate matter people having a greater risk of poor health.

RO2: We should determine if safety training was indeed practical for staff

The statistical Testing showed that safety training was indeed practical for the team. The employees should be trained to reduce any work-related injuries and safety precautions in the workplace.

RO3: Determine if Sun Coast received a return of investment for the services offered to customers

The statistical Testing illustrates that the Sun Coast had a significant mean from the other groups on investment; hence the firms received a return on investment.

RO4: We should next determine how much lead exposure employees are contaminated with lead

The statistics testing showed low levels of lead exposure to the staff. Although there are no recommended levels of zinc exposure, the low levels illustrate that the organization has achieved it.

RO5: Determine how sound level exposure affects employees' hearing.

The sound level exposure may affect the employee's hearing and hence impact productivity. Organizations need to control the employee exposure to the sound levels. If they cannot control noise from outside, they need to provide employees with hearing devices to limit the excess noise pollution.

RO6: Determine how practical new hire training is working

The statistics significantly illustrate that new hire training is based on how the employees effectively settle within the organizations and carry out their daily activities.

The US exposure rates to delicate matter such as fine PM2 can be considered safe via the US environmental protection agency's national ambient air quality standards. However, individuals have to breathe a limit of up to 12 micrograms per cubic meter of air (ug/m3).

It is essential to carry out safety training since the employees must have technical knowledge on handling equipment in the workplace and avoid injuries.

There recommended NIOSH exposure limit for occupational notices is 85 decibels. It is recommended to utilize hearing protection in the event the hazardous noise levels are not adequately reduced.

A business must train the new staff on proper safety and PPE use, including protective equipment like earplugs, safety goggles, lockout ladders, safely wipe up any spills, and other helpful training techniques to reduce the instance of injuries.

It is crucial to understand that there is no safe blood level of lead, but a five mcg/dl can be used to illustrate unsafe levels for children, and hence the blood levels need to be tested periodically.

Investors must expect some realistic return for their investment, and a good return on investment is considered about 7% per annum.