InstructionsCreate a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format:§ Slide 1: Include a title slide.§ Slide 2: Organize the agenda.§ Slide 3: Introduce the project.o Statement of the Problemso Research Objectives§ Slide 4: Describe information gathered from the literature review.§ Slide 5: Include research methodology, design, and methods.o Research Methodologyo Research Designo Research Methodso Data collection§ Slide 6: Include research questions and hypotheses§ Slides 7 and 8: Explain your data analysis.§ Slides 9 and 10: Explain your findings.§ Slide 11: Explain recommendations including an explanation of how research-based decision-making can directly affect organizational practices.§ Slide 12 and 13: Reflect on your experience throughout the course. Provide some of the things you learned and some of the course’s takeaways that you can apply to your current or future job.§ Slide 14: Include references for your sources.Your PowerPoint must be a minimum of fourteen slides in length (including the title slide and a reference slide).You are required to narrate your presentation. Utilize the note section to write out your transcript per slide. Ensure the presentation you create is your own authentic work. Ensure that you follow APA guidelines and cite any resources you use. For assistance with adding narration to your presentation, click here for an instructional document.

Instructions Create a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format: § Slide 1: Inclu

Running head: ADDRESSING SUN COAST’S PROBLEMS THROUGH RESEARCH 0 Addressing Sun Coast’s Problems through Research Columbia Southern University Literature Review (Song et al., 2015) are the faculty members of Tsinghua University, Beijing in the school of environment. The author believes that global sustainable remediation has become one of the leading trends in the contaminated land remediation field. Therefore, the main purpose of the study is to develop an effective sustainable remediation assessment indicator applicable in China including all other countries of the world. The tools like correlation and ANOVA have been used in the study for quantitative design research. The study made use of the LCA method applying the indicator to remediation mega-site in China for evaluating the impact over waste, worker safety, local impact, and resources. The study delivers a result that the indicator is appropriate for ensuring green and sustainable remediation. Sun Coast benefits from the study as it could use the same indicator for improving the safety of its workers during their work on the contaminated sites; thus, putting a positive impact on the organization. (Cappuyns, 2016) is currently working at the Centre for Economics and Corporate Sustainability in Belgium. The study attempts to evaluate the involvement of social aspects in the sustainability assessment of remediation projects. It makes use of decision support tools in the sustainability assessment of remediation project for analyzing the consideration of social aspects in the same tools. The study makes use of the ANOVA & t-test method for its quantitative design research to meet its objectives. The study finding reveals that the level of social factors involved in the relevant indicator is high in terms of human health and safety, neighborhood and locality, ethics, and equality. However, it is low in terms of uncertainty and evidence and specific legislation. The study results are beneficial for Sun Coast as it could make use of the same decision support tool for evaluating the sustainability at their remediation sites to meet its objectives; thus, putting a positive impact on the organization. (Damalas & Eleftherohorinos, 2011) are the present faculty members of the University of Thrace and the University of Thessaloniki at the Department of Agriculture. The study attempts to evaluate the effective use of pesticides at the remediation sites along with the application of alternative coping systems through regression and correlation method for its quantitative design research. The results of the study provide information regarding the appropriate use of cropping and pesticide utilization at the remediation sites for reducing the adverse impact on human health and the environment. The study carries extreme significance for the Sun Coast as the organization could make use of the same methodology for achieving its health and safety objectives oriented around their employees; thus, putting a positive impact on the organization. (Tang et al., 2012) are currently the faculty members in the school of environmental science and engineering at Sun Yat-Sen University, Guangzhou. The study attempts to evaluate, compare, and analyze the non-food energy and fiber plants carrying the potential of supplying excessive renewable energy resources and economic benefits at the remediation sites to the organizations. The study makes use of regression and correlation methods for its quantitative design research to evaluate the current options. The study finding reveals the information regarding the soil types, plants, and agronomic activities carrying the potential of boosting economic results for the organization from a remediation site. The study results are valuable for Sun Coast as the company could make use of the same strategies for improving their productivity; while ensuring appropriate safety to its employees; thus, putting a positive impact on the growth and success of the organization. (Hou & Al-Tabba, 2014) are the faculty members at the Department of Energy at the University of Cambridge. The authors state that the land is a critical element for the life support system as well as for the production of economic systems. The purpose of the study is to carry out an evaluation of sustainable remediation for the effective remediation of the project sites by developing and implementing various effective, norms and standards for practitioners. The study makes use of regression, t-test, and ANOVA for its quantitative design research. The findings of the study reveal that sustainability must be ensured for all the environmental remediation sites as it owes various effective implications towards regulation, liability owners, technology vendors, contractors, and consultants. The study carries much value for Sun Coast as the organization could make use of sustainable approach identified in the study for improving the productivity of their site along with providing effective health and safety care to its employees; thus, putting a positive impact on the organization. (Ridsale & Noble, 2016) are the faculty members at the Department of Geography and Planning at the University of Saskatchewan, Canada. The main purpose of the study is to investigate the sustainability in the remediation frameworks for providing effective guidance regarding practical implementation. The study makes use of correlation, regression, and ANOVA method in its quantitative design research to meet the objectives. The findings of the study provide information that there are not perfect criteria for remediation sites as trade-offs are always present. Therefore, the organizations working on remediation sites must properly analyze the trade-offs of making the right selection. The findings of the study are effective for the Sun Coast as the organization could analyze and select the appropriate criteria for meeting its health and safety objectives towards its employees; thus, putting a positive impact on the organization. References Cappuyns, V. (2016). Inclusion of social indicators in decision support tools for the selection of sustainable site remediation options. Journal Of Environmental Management, 184, 45-56. doi: 10.1016/j.jenvman.2016.07.035 Damalas, C., & Eleftherohorinos, I. (2011). Pesticide Exposure, Safety Issues, and Risk Assessment Indicators. International Journal Of Environmental Research And Public Health, 8(5), 1402-1419. doi: 10.3390/ijerph8051402 Hou, D., & Al-Tabbaa, A. (2014). Sustainability: A new imperative in contaminated land remediation. Environmental Science & Policy, 39, 25-34. doi: 10.1016/j.envsci.2014.02.003 Ridsdale, D., & Noble, B. (2016). Assessing sustainable remediation frameworks using sustainability principles. Journal Of Environmental Management, 184, 36-44. doi: 10.1016/j.jenvman.2016.09.015 Song, Y., Hou, D., Zhang, J., O’Connor, D., Li, G., & Gu, Q. et al. (2018). Environmental and socio-economic sustainability appraisal of contaminated land remediation strategies: A case study at a mega-site in China. Science Of The Total Environment, 610-611, 391-401. doi: 10.1016/j.scitotenv.2017.08.016 Tang, Y., Deng, T., Wu, Q., Wang, S., Qiu, R., & Wei, Z. et al. (2012). Designing Cropping Systems for Metal-Contaminated Sites: A Review. Pedosphere, 22(4), 470-488. doi: 10.1016/s1002-0160(12)60032-0

Instructions Create a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format: § Slide 1: Inclu

Running Head: UNIT III SCHOLARLY ACTIVITY 0 Unit III Scholarly Activity Columbia Southern University Research Methodology, Design, and Methods Sun Coast has a challenge in promoting the safety of its employees. The management of the Sun Coast has not established effective measures to curb the impacts of lead on its staff. The management of the Sun Coast must come up with means of addressing the safety issues that it is facing. In this discussion, the problems facing the Sun Coast will be reviewed by conducting thorough research. The research methodology that will be employed here is quantitative research. The quantitative research design focus will be descriptive in nature. This scientific method of analyses will be used in this study to allow statistical data collected to be analyzed properly avoiding misinterpretation and manipulation of resulting facts. Research Methodology The form of research methodology utilizing numerical data, statistical values, and documentation of phenomena known quantitative research will be employed here is a positivist worldview. The positivist worldview focuses on hypotheses, dissecting data to determine its justification or explanation based on actual events and facts which validate their existence. According to the positivist worldview, science is the sole way of learning about the truth. The positivism worldview stresses that it is only the “truthful” knowledge acquired through observation, including measurement, which is trustworthy (Nelson, 2018). There are various reasons why the positivist worldview will be employed in this research. First, it will make the research more reliable as it relies on quantitative data. Secondly, the positivist worldview is more scientific in its research methodology hence producing trustful study results. Additionally, the positivism provides objective data that researchers can utilize in making scientific assumptions. The information used in its study will be useful in addressing various safety and health concerns of the company. Research Design The research design is descriptive in nature. The project utilizes a descriptive design because it allows the researcher “to gather quantifiable information that can be used to statically analyze a target audience or particular subject” (CIRT, 2019). The information collected will be analyzed scientifically resulting in data controls voided of behavioral influences or manipulation. Research Methods The study will employ a correlative research method with a descriptive research design. Correlative research is crucial in testing the relationship between two variables (Creswell & Creswell, 2018). It is the analysis of “two variables and assesses the statistical relationship between them with little or no effort to control extraneous variables” (Price, Jhangiani, & Chiang, 2015). The variables that will be tested in this study include time hours lost while training and training expenses, PM and health impact on the employees, revision of the various training programs present and prior training programs that have been archived, of blood levels of the employees increased and the lead poisoning on sites, returns on investment and the services that render financial gain, and desired level of work environment and the noise level environment that exist. The primary reason as to why a correlation research method was employed is due to its effectiveness in measuring the relationship between different variables. Correlational research is also crucial in determining how an incident is caused (Creswell & Creswell, 2018). Further, the correlation research compares the variables between collected data and previously collected data without the help of other sources of information from various studies. The scientific method of research has proved to be very reliable due to its effectiveness in all types of organizational studies (Savkovic-Stevanovic, 2017). Data Collection Methods The primary method employed in data collection is observation (Harwoodn & Hutchinson, 2018). The employees of the Sun Coast will be observed while in their daily organizational operations. Interviews will also be used in collecting data. The employees and the management of the Sun Coast will be interviewed. The meetings will be focused on underpinning the issues facing the employees on Sun Coast. Other methods of collecting data are the analysis of archival data collected previously from the organization which will help in identifying established health and safety measures of the organization. Sampling Design The sampling design that will be utilized in this research is the convenience sample (Creswell & Creswell, 2018). A convenience sample consists of people who are reached quickly. Convenience sampling was used in this study due to several reasons. First, it will save time and money as the subjects will be easily located. Secondly, it will ensure that the data is readily available. Moreover, the sampling method is crucial in pilot studies. Data Analysis Procedures The data analysis procedure will employ various methods. First, a simple regression will be used. Simple regression utilizes the values from available data set comprising of measurements of the two variables in developing a model that helps predict the dependent variable amount (Creswell & Creswell, 2018). Example: This procedure will test the RQ1 hypotheses to deconstruct the data, pinpoint the location of controlled values that determine whether statistically, a lack of safety training will have an impact on the number of lost time hours for employees. Secondly, the multiple regression will be applied in learning the more about the link between independent and dependent variables (Creswell & Creswell, 2018). Example: This procedure will test the RQ2 hypotheses because the multiplication of data sources can change the outcome of a single hypothesis. Multiple regression data collected will be applied from former contracts to make useful decisions to engage the decibel ranges of Sun Coast work sites/environments. Thirdly, the independent t-test will be involved in comparing the means of two separate groups to conclude whether there exists a statistical proof that the related population means are considerably different (Creswell & Creswell, 2018). Example: This procedure will test the RQ3 hypotheses to determine the average scores of the previous training to compare with the scores of the active training, and then determine which one is more effective. Additionally, a paired sample t-test will be exercised to validate and signify the existence in differences between two observations will equal zero means. Example: This procedure will test the RQ4 hypotheses to determine if two separate groups and on different occasions was tested before and after working in a toxic environment at the organization. Finally, the one -way ANOVA testing will be used to determine whether there are any statistically significant differences among the study groups (Creswell & Creswell, 2018). Example: This procedure will test the RQ5 hypotheses to determine if all lines of service offer the same return on investment. References Center of Innovation in Research and Teaching. (2019). Overview of Descriptive Research. Retrieved from https://cirt.gcu.edu/research/developmentresources/research_ready/descriptive/overview Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approach (5th ed.). Thousand Oaks, CA: Sage Harwoodn, E., & Hutchinson, E. (2018). Data Collection Methods Series Part 5. Journal Of Wound, Ostomy And Continence Nursing, 36(5), 476-481. DOI: 10.1097/won.0b013e3181b35248 Nelson, E. S. (2018). Dilthey and Carnap: The Feeling of Life, the Scientific Worldview, and the Elimination of Metaphysics. The Worlds of Positivism, 321-346. doi:10.1007/978-3-319-65762-2_12 Price, P., Jhangiani, R., Chiang, I. (2015). Overview of Nonexperimental Research. Retrieved from https://opentextbc.ca./researchmethods/chapter/correlational-research/ Savkovic-Stevanovic, J. (2017). Modeling Method in the Scientific Research. Science Research, 3(3), 66. DOI: 10.11648/j.sr.20150303.14

Instructions Create a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format: § Slide 1: Inclu

Running head: SUN COAST CORRELATION AND REGRESSION ANALYSIS 0 Sun Coast Correlation and Regression Analysis Columbia Southern University Data Analysis: Hypothesis Testing Correlation and Regression tests are two parametric approaches that create data that show and describe the relationship and differences between groups, populations, or samples. The correlation tests and regression tests have been applied to the Sun Coast Safety Project. (Creswell & Creswell, 2018). Correlation: Hypothesis Testing Ho1: The first hypothesis is that there is no statistical significance between micron and the mean annual sick days per employee. Ha1: The second hypothesis that is being tested is that there is a statistical significance between micron and the mean annual sick days per employee. Correlation data Column 1 Column 2 Column 1 Column 2 -0.71598 A Pearson correction coefficient of r=0.716 shows that there is a moderately strong positive correlation. It equates to an r2 of .51, which exemplifies 51 % of the variance between the independent and dependent variables. Using an alpha of .05, the test result indicates there is a p-value of 1.05 < .05. Therefore, the null hypothesis is accepted, and the alternative hypothesis is rejected that there is a statistically significant relationship between micron and the number of sick leaves. The P-value and multiple R were obtained by running the data on sheet one using simple regression. Simple regression for sheet one Regression Statistics Multiple R 0.719543 R Square 0.517742 Adjusted R Square 0.512919 Standard Error 1.299576 Observations 102 ANOVA df SS MS F Significance F Regression 181.3162 181.3162 107.3578 1.59919E-17 Residual 100 168.8897 1.688897 Total 101 350.2059 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 10.01017 0.309974 32.29362 1.05E-54 9.395194602 10.62515 9.395195 10.62515 -0.51501 0.049705 -10.3614 1.6E-17 -0.613625981 -0.4164 -0.61363 -0.4164 Simple Regression: Hypothesis Testing Ho2: There lacks a statistical relationship between safety training hours and lost time hours as the predicted outcome Ha2: There is a statistical relationship between safety training hours and lost time hours as the predicted outcome. 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 Significance F Regression 1008202 1008202 1664.211 7.6586E-105 Residual 221 133884.9 605.814 Total 222 1142087 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 273.4494 2.665262 102.5976 2.1E-188 268.1968373 278.702 268.1968 278.702 X Variable 1 -0.14337 0.003514 -40.7947 7.7E-105 -0.150293705 -0.13644 -0.15029 -0.13644 The multiple r coefficient of r= 0.939 shows that there is a very strong correlation between lost time hours and safety and training. This equates to a multiple R of .88, explaining 88 % of the variance between the independent and dependent variables. With a large Anova F value of 1664. 211, there is a clue however that something is also significant in the relationship between the independent and dependent variables. Using an alpha of .05, the results indicate a p-value of 2.1< .05. A larger than 0.05 P-value indicates that the values do not fit well into the line (Zou, Tuncali, & Silverman, 2003). Therefore, the null hypothesis is accepted, and the alternative hypothesis is rejected that there is a statistically significant relationship between safety training hours and lost time hours. The x variable coefficient indicates a p-value of 7.7<.05, a statistical result that confirms that it is not statistically significant in the regression model. Dv=273.4494+-0.14337, which indicates that the model is nor predictive Multiple Regressions: Hypothesis Testing Ha3: There is no statistical relationship between decibel as the predicted outcome and frequency, angle in degree, chord length and velocity. Ha3: There is a statistical relationship between decibel as the predicted outcome and frequency, angle in degree, chord length and velocity. SUMMARY OUTPUT Regression Statistics Multiple R 0.602018 R Square 0.362425 Adjusted R Square 0.360294 Standard Error 5.519422 Observations 1502 ANOVA df SS MS F Significance F Regression 25906.34 5181.267 170.0782477 2.0796E-143 Residual 1496 45574.18 30.46402 Total 1501 71480.51 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 126.8097 0.624161 203.1683 125.5853697 128.034 125.5854 128.034 800 -0.00112 4.76E-05 -23.4962 3.6392E-104 -0.001211363 -0.00102 -0.00121 -0.00102 0.046383 0.037337 1.242292 0.214323474 -0.02685487 0.119621 -0.02685 0.119621 0.1809 -5.41565 2.930439 -1.84807 0.064789628 -11.16386013 0.332552 -11.1639 0.332552 71.3 0.083527 0.00931 8.971829 8.51468E-19 0.065265095 0.101789 0.065265 0.101789 0.002663 -240.385 16.52241 -14.549 5.9646E-45 -272.7947344 -207.976 -272.795 -207.976 The multiple R coefficient of R= .60 indicates a moderately strong correlation between decibel as the predicted outcome and frequency, angle in degree, chord length and velocity. This equates to an R2 of .36, explaining 36 % of the variance between the variables being tested. Using an alpha of .05, the results indicate a p-value of 0.21 < .05. Therefore, the null hypothesis is rejected, and the alternative hypothesis is accepted that there is a statistically significant relationship. The x variable coefficients indicate a p-value of 0.21, 0.06, for frequency and chord length respectively <.05 which shows that there are statistically significant in the regression model. The other three variables of frequency, velocity, and distribution have a greater significance level than their alpha values hence are statistically insignificant in the regression model (Levine, Berenson, Stephan, & Lysell, 1999). A significantly large Anova F statistic of 170.08 indicates that some variables are statistically significant in the prediction model. Therefore, the derived predictive model is: Dv=0.046383+-5.4, which indicates that the model is nor predictive. All the other variables are excluded because they have no statistical significance. References Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage. Levine, D. M., Berenson, M. L., Stephan, D., & Lysell, D. (1999). Statistics for managers using Microsoft Excel (Vol. 660). Upper Saddle River, NJ: Prentice-Hall. Zou, K. H., Tuncali, K., & Silverman, S. G. (2003). Correlation and simple linear regression. Radiology, 227(3), 617-628.
Instructions Create a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format: § Slide 1: Inclu
ANOVA and T-test Empirical research requires an investigator at some time to test whether the hypothesis they made should be rejected or accepted. ANOVA and T-test are the parametric statistical measures that are commonly used in the dispensation. ANOVA is often used to compare the mean of two or more groups, while the T-test is used when only two groups are involved (Rojewski, Lee, and Gemici). The T-test is preferable when comparing the mean between two groups, with the aim of identifying whether the population means of both samples greatly differ from one another. An example would include comparing whether monetary incentives are related to employee turnover. The investigator in the instance may collect data from both the test group and the control or placebo group. In order to confirm whether the resulting data sets are from the same sample population, a T-test is conducted to confirm how far the mean differs between the two data sets. The use of the T-test in the instance assumes, however, that the variable is normally distributed, but may also have unknown variances. ANOVA, on the hand, is appropriate for use when there are more than two groups that are being compared. In the case of employee turnover, the investigator may choose to collect data on three groups mainly, permanent, casuals, and the control group. Results may indicate that permanent employees have a high percentage of turnover, but also at a very low standard of deviation, while casuals have low turnover at a high standard deviation. In order to eliminate the effect of unknown variance, ANOVA is appropriate to determine how far among each group were the means different. That being said, the T-test is only appropriate to use when a small sample is involved and the groups to be compared are equal to two. References Rojewski, Jay, In Heok Lee, and Sinan Gemici. "Use of t-test and ANOVA in career-technical education research." Career and Technical Education Research 37.3 (2012): 263-275.
Instructions Create a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format: § Slide 1: Inclu
Running head: SUN COAST REMEDIATION RESEARCH PROJECT 0 Sun Coast Remediation Research Project Name University RESEARCH OBJECTIVES The main objective of the Sun Coast remediation is to ensure that the welfare and the safety of the employees are catered for. This research that will be done to ensure that the company get valuable information that will ensure that the employee’s welfare, health and safety are carefully considered. It will also ensure that the worksite injuries that might occur are also minimized. The company put about ten safety risks and health concerns that affect the welfare of the employees at the work station. This matter will thus require well done research. This study that I about to do will be able to determine if there is a significant relationship that involves the particulate with the exposure and the impacts that the PM has on the employee’s relatively to their health, lead poisoning, noise levels and training. The objective is to make a comparison on the outdated safety modules and data with its newly formed training equipment that are safe so as to help in determining its effectiveness and its consistency with the reduced lost time hours and the prediction when it comes to training activities. The main objective of the organization is to test the data condition and make appropriate decisions on the basis of return on investments that is concerned with the training as well as with the testing that has to do with the land sites. The return on investments will be comprise of the safety training programs of the organization, the quality of air and monitoring control, water recovery programs and lastly the soil outcome on testing. RO1: To establish whether the lost-time hours are related to safety training at the work place. RO2: To establish whether there exists relationship between variables that exist between the size of the particulate matter and the impact on the health of the employees. RO3: To establish whether training programs that is done together with revision is more effective than the prior training programs. RO4: To establish whether the blood lead level has been increasing in employees working at the site. RO5: To establish the existence of the return-on-investment controls that exist within the services that are provided. RO6: To establish through the data that are collected if the desired level of healthy work environment is predicted. RESEARCH QUESTIONS AND HYPOTHESIS The following research questions were carefully formulated after a careful review of the data that was collected. The research questions and the hypothesis that will help the researcher to achieve the desired outcomes were written below. RQ1: What is the relationship that exist between expenses on the safety and training program with the reduction of lost-time hours? H01: There is no significant correlation between lost hours while training and the expenses on training. HA1: There is a significant correlation between lost time hours while training and the expenses on training. RQ2: What is the relationship between variables determining the size of PM and the health impact on the employees? H02: There is no significant correlation between variables of PM and health impact on the employees. HA2: There is a statistically significant correlation between variables of PM and the health impact of the employees. RQ3: What is the relationship between the revision of the various training programs and the training that is archived? H03: There is no significant correlation between the revision associated with the training programs of group A scores with the archived training programs of group B scores. HA3: There is a significant correlation between the revision associated with the training programs of group A scores with the archived training programs of group B scores. RQ4: Determine the relationship between the level of blood of the employees and lead poisoning? H04: There is no statistical correlation between the level of blood of the employees and lead poisoning. HA4: There is a statistical correlation between the level of blood of the employees and lead poisoning. RQ5: Determine the relationship between return on investment and the services that are used to evaluate the financial gain? H05: There is no statistical correlation that exists between returns on investment and the services that render financial gain? HA5: There is a statistical correlation between returns on investments and services that are used to render financial gains. RQ6: Determine the relationship that exists between the desired level of a work environment and the noise level of environment? H06: There is no significant correlation between desired level of work environment and the noise level environment that exist. HA6: There is a significant correlation between desired level of work environment and the noise level environment that exist. S Environmental Impact Statements for Noise. (1976). The Impact of Noise Pollution, 419-424. doi:10.1016/b978-0-08-018166-0.50036-1 Hadfield, L. (2011). Health hazard evaluation report: HETA-2008-0155-3131, lung function (spirometry) testing in employees at a flavorings manufacturing plant - Indiana. doi:10.26616/nioshheta200801553131 Toxicity Assessment. (n.d.). doi:10.1002/(issn)10982256 Wells, A. T., & Hopper, P. L. (1992). Measuring Hearing Protection Device Performance Using the Metrosonics db-3100 Sound Level Analyzer(Dosimeter). doi:10.21236/ada260852 Evaluation of nonproduction area air and surface lead levels, employee blood lead levels, and psychosocial factors at a battery manufacturing plant. (2018). doi:10.26616/nioshhhe201302263314 Zhan, C. (2019). Health Services Information: Patient Safety Research Using Administrative Data. Health Services Evaluation, 241-264. doi:10.1007/978-1-4939-8715-3_12
Instructions Create a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format: § Slide 1: Inclu
Running head: DESCRIPTIVE STATISTICS 0 Descriptive statistics Stephener Baisey Columbia Southern University Data Analysis Descriptive Data and Assumptions: Correlation Frequency Distribution Table PM size Frequency 0-1 2-4 24 5-7 37 8-10 34 Sick Days Frequency 0-2 4-7 61 8-9 30 10-12 11 Histogram Descriptive Statistics Table microns sick day Mean 5.65728155 Mean 7.126214 Standard Error 0.25560014 Standard Error 0.186484 Median Median Mode Mode Standard Deviation 2.59405814 Standard Deviation 1.892605 Sample Variance 6.72913764 Sample Variance 3.581953 Kurtosis -0.8521619 Kurtosis 0.124923 Skewness -0.37325713 Skewness 0.14225 Range 9.8 Range 10 Minimum 0.2 Minimum Maximum 10 Maximum 12 Sum 582.7 Sum 734 Count 103 Count 103 Largest(1) 10 Largest(1) 12 Smallest(1) 0.2 Smallest(1) Confidence Level (95.0%) 0.50698167 Confidence Level (95.0%) 0.36989 Kolmogorov-Smirnov Test The hypotheses used are: Ho: The sample data provided has no significant difference to the data that relates to normal population. H1: There is a significant difference that emerges between the sample data to that of normal population. Use an alpha of .05 and provide the test statistic and p level here P > 0.05 P ≤ 0.05 Accept or reject the null hypothesis here. The null hypothesis is rejected Measurement Scale Ordinal Measure of Central Tendency Mean Evaluation The above descriptive statistics has indifferences as the test static of the sample data to that of normal population were different. Assumptions for parametric testing The assumptions in the parametric testing were not met as there was indifferences in the results under a 95 percent confidence interval. First there was differences in the data which led to differences in the measures of central tendency. For instance, the mean of the data for microns and sick day as projected by that of 5.65 and that of 7.12 respectively. Despite having similar counts that was also a difference that arose between the highest and lowest number in the data provided. Additionally, parameters in the test static for the two populations gave contrastive results. Thus, the assumptions in the parametric testing remained unmet. Descriptive Data and Assumptions: Simple Regression Frequency Distribution Table Expenditure Frequency 20-500 108 501-1000 76 1001-1500 27 1501-2000 11 2001-2500 Time Frequency 0-50 51-100 26 101-200 98 201-300 85 301-400 Histogram Descriptive Statistics Table safety training expenditure lost time hours Mean 595.9843812 Mean 188.0045 Standard Error 31.4770075 Standard Error 4.803089 Median 507.772 Median 190 Mode 234 Mode 190 Standard Deviation 470.0519613 Standard Deviation 71.72542 Sample Variance 220948.8463 Sample Variance 5144.536 Kurtosis 0.444080195 Kurtosis -0.50122 Skewness 0.951331922 Skewness -0.08198 Range 2251.404 Range 350 Minimum 20.456 Minimum 10 Maximum 2271.86 Maximum 360 Sum 132904.517 Sum 41925 Count 223 Count 223 Largest(1) 2271.86 Largest(1) 360 Smallest(1) 20.456 Smallest(1) 10 Confidence Level (95.0%) 62.03197147 Confidence Level (95.0%) 9.465484 Kolmogorov-Smirnov Test State null and alternative hypotheses for normality here. H0: The sample data that relates to training expenditure is different to that of lost time hours. H1: There is a significant difference value between the data in training expenditure and that of lost time hours. Use an alpha of .05 and provide the test statistic and p level here P > 0.05 P ≤ 0.05 Accept or reject the null hypothesis here. We accept the null hypothesis Measurement Scale Nominal Measure of Central Tendency Median Evaluation The p value for both training expenditures and the lost time hours is exceedingly high. Assumptions for parametric testing The assumptions for parametric testing in the study prove to be met as expressed by the test statistic. First, there is a huge difference that emerges in the data between the training expenditure and the lost time hours. Findings from the statistical test indicates that the p value in both the training expenditure and lost time hours is exceeding high. However, an analysis of the data indicates that there lost time hours has a smaller confidence interval as opposed to that of training expenditure. Thus, the statistical tests proves the assumptions as there is a great difference that emerges in the two data sets. Descriptive Data and Assumptions: Multiple Regression Frequency Distribution Table Decibel Frequency 100-106 107-111 51 112-116 126 117-121 249 122-131 786 132-141 287 Histogram Descriptive Statistics Table Decibel Mean 124.8359 Standard Error 0.177945 Median 125.721 Mode 127.315 Standard Deviation 6.898657 Sample Variance 47.59146 Kurtosis -0.31419 Skewness -0.41895 Range 37.607 Minimum 103.38 Maximum 140.987 Sum 187628.4 Count 1503 Kolmogorov-Smirnov Test State null and alternative hypotheses for normality here. H0: There is no relationship between the X and Y variables. H0= H1=0 H1 ≠ 0 Use an alpha of .05 and provide the test statistic and p level here P=0 P>0 Accept or reject the null hypothesis here. Reject Measurement Scale Internal Measure of Central Tendency Mean Evaluation There is no direct relation between the variables. Assumptions for parametric testing The assumptions for parametric testing were unmet as it is evident that is no relationship between the variables. In such circumstances there is a null hypothesis for each variable an indication that the variables do not fit in the multiple regression equation. Since the variable do not have any relations there remains a standard error in the data. Since the null hypothesis was untrue there is less probability of obtaining a test statistic based on the data provided. This is because there are two variables at the expense of three. This makes the parametric assumptions to remain unmet as there is no clear relationship. Descriptive Data and Assumptions: Independent Samples t Test Frequency Distribution Table Training Frequency 49-60 12 61-70 20 71-80 21 81-90 91-100 Training Frequency 74-80 14 81-85 21 86-90 19 91-95 96-100 Histogram Descriptive Statistics Table Prior Training Revised Training Mean 69.79032 Mean 84.77419 Standard Error 1.402788 Standard Error 0.659479 Median 70 Median 85 Mode 80 Mode 85 Standard Deviation 11.04556 Standard Deviation 5.192742 Sample Variance 122.0045 Sample Variance 26.96457 Kurtosis -0.77668 Kurtosis -0.35254 Skewness -0.0868 Skewness 0.144085 Range 41 Range 22 Minimum 50 Minimum 75 Maximum 91 Maximum 97 Sum 4327 Sum 5256 Count 62 Count 62 Largest(1) 91 Largest(1) 97 Smallest(1) 50 Smallest(1) 75 Confidence Level (95.0%) 2.805048 Confidence Level (95.0%) 1.31871 Kolmogorov-Smirnov Test State null and alternative hypotheses for normality here. H0=0 H1>0 Use an alpha of .05 and provide the test statistic and p level here P≠ 0 Accept or reject the null hypothesis here. Accept Place detailed test data in the appendix. Measurement Scale Internal Measure of Central Tendency Mean Evaluation There is an indirect relationship between the sample data and the normal population. Assumptions for parametric testing. The assumptions were met. Statically test indicate that the probability test is lower than the p value. For instance, in the first data the p value is 2.8 whereas the second data has a p value of 1.31. The p value is greater than 0. This indicates that there is a indirect relationship of the data as evidenced by the p value. The dependent variables were normally distributed. Additionally, there are two groups which are independent to each other such as the test scores for the revised training and that of prior training. Therefore, there is an indirect relationship of the data provided. Descriptive Data and Assumptions: Dependent Samples t Test Frequency Distribution Table Exposure Frequency 5-15 16-25 26-35 12 36-45 16 46-56 Exposure Frequency 5-15 16-25 26-35 11 36-45 17 46-56 Histogram Descriptive Statistics Table Pre-Exposure μg/dL Post-Exposure μg/dL Mean 32.8571429 Mean 33.28571 Standard Error 1.75230655 Standard Error 1.781423 Median 35 Median 36 Mode 36 Mode 38 Standard Deviation 12.2661458 Standard Deviation 12.46996 Sample Variance 150.458333 Sample Variance 155.5 Kurtosis -0.57603713 Kurtosis -0.65421 Skewness -0.42510965 Skewness -0.48363 Range 50 Range 50 Minimum Minimum Maximum 56 Maximum 56 Sum 1610 Sum 1631 Count 49 Count 49 Largest(1) 56 Largest(1) 56 Smallest(1) Smallest(1) Confidence Level (95.0%) 3.52324845 Confidence Level (95.0%) 3.581792 Kolmogorov-Smirnov Test State null and alternative hypotheses for normality here. Ho: u1=0 H1:u1≠0 Use an alpha of .05 and provide the test statistic and p level here α=0.05 t=m1-m2/Sd/n 33.28571-32.8571429=0.4285671/1.7523 t=0.24457 Accept or reject the null hypothesis here. Accept Measurement Scale Interval Measure of Central Tendency Mean Evaluation The null hypothesis is accepted as the null hypothesis is greater than 0. Assumptions for parametric testing The assumptions for parametric testing were met. This is because the data consisted of dependent variable which were continuous on a ratio basis. Additionally, the observations of the data collected were independent of one another. This is irrespective of the fact that dependent variables were normally distributed. A comparison of the two means indicates that there is statistical difference between the mean. In the data present the difference between the mean is 0.4285671. An evaluation of the statistical test indicates that the t-test is greater than the calculated test. The differences between the observed t test and the calculated t-test leads to acceptance of the null hypothesis. Descriptive Data and Assumptions: ANOVA Frequency Distribution Table Air Frequency 1-3 4-6 7-9 10-12 12-15 Soil Frequency 5-7 8-10 13 10-13 Water Frequency 1-3 4-6 10 7-9 10-12 Training Frequency 1-3 4-6 16 7-9 Histogram Descriptive Statistics Table A = Air B = Soil Mean 8.9 Mean 9.1 Standard Error 0.684028 Standard Error 0.390007 Median Median Mode 11 Mode Standard Deviation 3.059068 Standard Deviation 1.744163 Sample Variance 9.357895 Sample Variance 3.042105 Kurtosis -0.6283 Kurtosis 0.11923 Skewness -0.36085 Skewness 0.492002 Range 11 Range Minimum Minimum Maximum 14 Maximum 13 Sum 178 Sum 182 Count 20 Count 20 Largest(1) 14 Largest(1) 13 Smallest(1) Smallest(1) Confidence Level(95.0%) 1.431688 Confidence Level(95.0%) 0.816294 C = Water D = Training Mean Mean 5.4 Standard Error 0.575829 Standard Error 0.265568 Median Median Mode Mode Standard Deviation 2.575185 Standard Deviation 1.187656 Sample Variance 6.631579 Sample Variance 1.410526 Kurtosis -0.23752 Kurtosis 0.253747 Skewness 0.760206 Skewness 0.159183 Range Range Minimum Minimum Maximum 12 Maximum Sum 140 Sum 108 Count 20 Count 20 Largest(1) 12 Largest(1) Smallest(1) Smallest(1) Confidence Level (95.0%) 1.205224 Confidence Level (95.0%) 0.55584 Kolmogorov-Smirnov Test State null and alternative hypotheses for normality here. H0: There is no difference of the means. H1: Means are not all equal Use an alpha of .05 and provide the test statistic and p level here α=0.05 Test statistic of water to training (7-5.4)2=2.56 Test statistic of air to soil (8.9-9.1)2=0.0004 Accept or reject the null hypothesis here. Accept Measurement Scale Ratio Measure of Central Tendency Mean Evaluation The means are not equal as they base on different sets of data. Assumptions for parametric testing Based on the data provided the assumptions that can be derived are those for normality, equal variance and that of independent errors. From the data there is an interaction of the variables with no restrictions. The parametric assumptions in this scenario would relate to the parameters on the population distribution upon which data is drawn. Additionally, a non-parametric test would refer to that which makes no such assumptions. This leads to normal distribution, homogeneity of the variances, multiple groups which relates to the same variance as well as linearity on the independent relationships. Thus, the assumptions define the type of variance. References Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data analysis: A model comparison approach to regression, ANOVA, and beyond. Routledge. Kalaian, S. A., & Kasim, R. M. (2016). Analyzing quantitative data. In Mixed Methods Research for Improved Scientific Study (pp. 149-164). IGI Global.

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