Industrial Ergonomics

post is of two assignments

Objective:
• Demonstrate an ability to put into practice techniques, skills, and modern engineering tools learnt in school.
Procedures
The students will form groups of four (4). Each group will first identify a Methods and Industrial Ergonomics problem/opportunity (a real job performed by an operator(s)) that could be modeled, analyzed, and improved by using the software JACK. Once you identify the real job, you must obtain the details about the different tasks that are part of the job (worker(s) characteristics, cycle time(s), workstation(s) dimensions, etc.) – everything to properly model the task in JACK. Then using the proper analyses (postural, energy, biomechanical, NIOSH, BasicMOST, etc) find out what opportunities for improvements exist for the job. Finally, you need to show in JACK the improvements to demonstrate that your proposed solution work. Your project must include the use of digital human modeling using the Jack software to visually and analytically represent and improve the job selected.
Project Deadline
The presentation of your projects is going to be on May 2, 2018 between 3:00 to 5:50 pm (lab time). A copy of your PowerPoint presentation must be submitted electronically using Blackboard not later than May 2, 2018 at 3:00 pm.

Presentation
Each team will prepare a 10-12 minutes presentation to be given out of the normally scheduled lab period (your presentation must include screens and simulations using the Jack software).

Report
Your project reports are due not later than May 2, 2018 at 3:00 pm (lab time). The report must be submitted electronically using Blackboard. There is no page limit for your written report, but you must submit a formal engineering report that includes:
Jack files:
A copy of your JACK files (environment, simulations, etc) must be placed on a folder and must be submitted electronically using Blackboard not later than May 2, 2018 at 3:00 pm. By running these files, we must be able to replicate your analyses and to verify that you used in the software the task and worker characteristics described in your report.
Suggested format for your report
Title page (including the name of the project, the course number and name, team members, date)
Introduction/Background (in this section you must describe the real task used for your project including all the elements mentioned earlier to describe the job selected)
Methodology (describe the rationale for using the task analyses used)
Results
Discussion/Recommendations
Software
You also must provide a copy of all JACK files used (environments, simulations, etc.) to be able to replicate your analyses and to verify that you used in the software the task characteristics described in your report.
Grading
To receive credit for your project must submit everything that was described above. Missing one component will result in a grade of 0 in your project.
Your project will be graded according to:
• Content
• Organization
• Adherence to guidelines specified in the project description.

identify a Methods and Industrial Ergonomics problem/opportunity (a real job performed by an operator(s)) that could be modeled, analyzed, and improved by using the software JACK. Once you identify the real job, you must obtain the details about the different tasks that are part of the job (worker(s) characteristics, cycle time(s), workstation(s) dimensions, etc.) – everything to properly model the task in JACK. Then using the proper analyses (postural, energy, biomechanical, NIOSH, BasicMOST, etc) find out what opportunities for improvements exist for the job. Finally, you need to show in JACK the improvements to demonstrate that your proposed solution work. Your project must include the use of digital human modeling using the Jack software to visually and analytically represent and improve the job selected.

2:Analysis of health dataset

Instructions: This assignment involves analyzing a health dataset (HSRS325_data.xlsx). This dataset includes a sample of 100 healthy young adult women. Refer to the coding manual for this dataset for help with interpreting the values provided for the different variables. You can complete this assignment using the on-line calculators posted on Moodle, or you may use Microsoft Excel or a statistical software package (e.g., SPSS) if you prefer.
Please name your file with the class number, your name, and the assignment name (e.g., 325_Smith_AnalysisProj.docx). Before you submit your assignment, make sure that you’ve answered each question clearly and completely. Partial credit will be given when appropriate.
You may discuss the assignment with your classmates, but do not complete the assignment together – the work you turn in must be your own. Any assignment that includes copied, paraphrased, or plagiarized material will receive a grade of zero.

1. Fill in the following table to describe the study population included in this dataset. Note that “CI” refers to confidence interval and “n” refers to sample size. For the “%” column, enter the percentage of study participants who fall into that category. [15 points]

Continuous Measures1 Mean ± SD 95% CI of the Mean
Age (years)
Weight (lbs)
Body mass index (kg/m2)
Waist circumference (inches)
Body fat percentage
Caloric intake (kcal)
Physical activity (METs/wk)
Categorical Measures n %
Race
White
Black
Other
Education level
High school degree
Some college, no degree
College degree
Some grad school, no degree
Graduate degree
Current OC users
Ever smokers

2. Take a look at the values you entered for physical activity. What does the range defined by the mean plus/minus the standard deviation tell you? [3 points]

3. Take a look at the values you entered for physical activity. What does the range defined by the 95% confidence interval of the mean tell you? [3 points]

4. Fill in the following table to describe whether current OC users differ from non-current-OC users, including the missing sample sizes (n=?) for current OC users and non-users. The “test statistic” value would be the appropriate test statistic for the hypothesis testing process for this type of data. The P values should be two-sided. [15 points]

Mean ± SD Test
Statistic
OC Users
(n=?) Non-OC Users
(n=?) P Value
Age (years)
Weight (lbs)
Body mass index (kg/m2)
Waist circumference (inches)
Body fat percentage
Caloric intake (kcal)
Physical activity (METs/wk)

5. Provide a brief summary of the information presented in the table you completed in question 4. Are there any clinically/practically significant differences between OC users and non-users? Any statistically significant differences? [6 points]

6. Fill in the following correlation matrix to describe how different types of physical activity1 are related. Include the correlation coefficient (r) and the P value in each indicated cell of the matrix. [15 points]

Walking Jogging Running Bicycling Aerobics Swimming Yoga Resistance training
Total PA r (P) r (P) r (P) r (P) r (P) r (P) r (P) r (P)
Walking – r (P) r (P) r (P) r (P) r (P) r (P) r (P)
Jogging – – r (P) r (P) r (P) r (P) r (P) r (P)
Running – – – r (P) r (P) r (P) r (P) r (P)
Bicycling – – – – r (P) r (P) r (P) r (P)
Aerobics – – – – – r (P) r (P) r (P)
Swimming – – – – – – r (P) r (P)
Yoga – – – – – – – r (P)
1 Participation in each activity was measured in metabolic equivalents (METs) per week.

7. Take a look at your completed matrix in question 6 and write a few sentences describing the results you presented in this table. Which activities are most strongly correlated with each other? Are any of these statistically significant? Which activities are not correlated with each other? [6 points]

8. Your correlation matrix in question 8 shows the correlations between different physical activities. Look at the dataset and choose two other continuous variables that you feel might be correlated with one another. Write a research hypothesis describing how you expect these two variables to be related. Do you expect a positive correlation or a negative correlation? [4 points]

9. Calculate the correlation coefficient and associated P value for the variables you selected in question 8. What do these values tell you? Was your hypothesis correct? [4 points]

10. What is the regression equation for the relationship between BMI (independent variable) and body fat percentage (dependent variable)? What does the slope in this equation tell you? [6 points]

11. What does it mean when the slope of a regression line is zero? What does it mean when the slope of a regression line is not zero? Is the slope of the line you described in question 12 statistically significantly different from zero? [6 points]

12. What is the R2 value for the relationship between BMI and body fat percentage? What does this R2 value tell you? [4 points]

13. If you were asked to create a multiple linear regression model to predict body fat percentage, which variables in this dataset would you include in your equation? Justify your answer. [4 points]

14. How would the R2 value for the multiple linear regression model you proposed in question 13 compare to the R2 value you reported in question 12? Do you expect it would it be higher? Lower? Justify your answer. [4 points]

health dataset