MAT5212 - Turnitin Is A Tool For Students And Academics To Check The Originality Of Their Work - Biostatistics Assignment Help

Assignment Task

SUBMISSION GUIDELINES: 
1. This assignment has a total of 52 marks and constitutes 20% of the final unit grade. 
2. Each student has been assigned a unique data file for this assignment. Students must use their assigned data file to complete the assignment. This data file will be emailed to your ECU student email address by 12:00 pm (noon) 24th September 2020. If you do not receive your data file, please first check your junk mail folder in your email. If you are still unable to find your data file, please email your lecturer from your ECU student email account. 
3. Please save your assignment using the following filename structure: 
Surname_studentID_MAT5212_A2. 
4. All students are to hand in a softcopy typed up assignment via Turnitin on Blackboard. Solutions to all questions in the assignments must be presented in pdf file or your assignment will NOT be marked. 
5. Turnitin is a tool for students and academics to check the ‘originality’ of their work. It is strongly recommended that you submit your assignment (or a late draft) for a Turnitin preview at least 48 hours before the actual due date. This should allow you enough time to receive your Turnitin originality report, and if required, make changes to your work and review it once more. The more time you allow yourself, the less likely the chance that you will end up involved in a breach of academic integrity rules. Once you ‘Submit’ your assignment, you are agreeing that you understand university rules and expectations around academic integrity, writing and referencing and that you have taken all necessary steps to ensure your work is authentic and your own. 
6. All questions must be presented in the order given. Failure to do so will result in your 
assignment NOT being marked. 
7. Unless otherwise told to use SPSS, all calculations are to be carried out manually. All manually calculated answers are to be presented correct to 4 decimal places (make sure you understand the rounding rules). Be clear and concise. Show all of your working for hand calculation questions. Full marks will only be awarded to correct answers with complete solutions. There is no requirement to explore your data for questions relating to manual calculations. Worded answers need to be written or typed up in grammatically correct English. Failure to do so will result in deduction of marks. 
8. For SPSS related parts, copy only the relevant tables and charts from the SPSS output across to your solution document, and these must be interpreted appropriately and in the context of 
the study. You should save copies of your SPSS data files (.sav) and output file (.spv) as you may be asked to submit your SPSS output file (showing computations) in the future. 
9. For questions that require students to choose a hypothesis test, a mark of 0% will be awarded for an INCORRECT choice of test even if the application of the chosen test is correctly carried out. 
10. In the interest of fairness, extension of time for submission of the assignment will be given only in exceptional circumstances, and then only in accordance with University rules (see page 2). All requests for extension must be submitted for consideration before the due date of the assignment otherwise no extension will be granted. 
11. The deadline for this assignment is 11:00 pm AWST on Wednesday, 14th of October 2020. 
12. Late penalty in accordance to ECU policy: No more than one week (seven calendar days) late, a penalty of 5% of the maximum assignment mark for each calendar day late. A mark of zero is given if the assignment is more than one week late. Avoid incurring late penalty by submitting your assignment early. 
CONDITIONS FOR ASSIGNMENT EXTENSION 
In accordance with ECU policy, extensions are given on the following grounds and must include all appropriate supporting documentation: 
• Ill health or injury for an extended period of time – medical certificate is required; 
• Compassionate grounds – supporting documentation includes e.g. newspaper notice plus other evidence if not the same family name, e.g. marriage certificate; 
• Representation in sporting activities at a national or international level; 
• Representation in significant cultural activities; 
• Employment related intrastate, interstate and overseas travel – a letter from your employer, including your supervisors’ full contact details. 
The following factors will NOT be considered as grounds for extension: 
• Routine demands of employment; 
• Stress or anxiety normally associated with examinations, required assessments or any aspect of course work; 
• Routine financial support needs; 
• Lack of knowledge of the requirements of academic work; 
• Difficulties with English language; 
• Scheduled anticipated changes of address, moving home etc; 
• Demands of sport, clubs, social or extra-curricular activity other than those specified above; 
• Recreational travel (domestic or international); 
• Planned events such as weddings, birthday parties etc; 
QUESTION 1 
Vitamin D is called the sunshine vitamin. It is produced in skin cells upon exposure to UV light. A recent research project examined how the amount of sun exposure related to Vitamin D levels in the blood. The data for this question can be found in the ‘Q1_VitD’ sheet of your unique student data file. 
(a) Define the predictor and response variables in this study. Provide an explanation.  
(b) Manually calculate the sum of square totals for Sxx, Syy, and Sxy.  
(c) Manually calculate the correlation coefficient and coefficient of determination.  
(d) Find the least-squares estimate for the regression of the effect that sunshine exposure has on Vitamin D levels. Write the equation. 
(e) Use SPSS to verify your results and to test the hypotheses for whether β0 = 0 and β1 = 0. The Model Summary and Coefficients SPSS output tables must be provided to support your results. 
(f) Use information presented in the SPSS outputs to determine the 95% confidence interval of the mean and the 95% prediction interval for the expected amount of Vitamin D produced when receiving 10 units of sunshine. 
(g) What are the assumptions associated with using simple linear regression? Verify these assumptions using SPSS. 
QUESTION 2 
It is recommended that adults consume 5 – 9 servings of fruit and vegetables per day. A previous survey showed the distribution of number of days per week that adults fulfilled their fruit and vegetable requirement. Following a short public health campaign, another survey was conducted. 
Manually perform a hypothesis test at the 5% level of significance to determine whether the distribution of number of days per week that adults fulfilled their fruit/vegetable requirement is the same as before the public health campaign. Data for this question can be found in the ‘Q2_FruitVegIntake’ worksheet of your data file.  
QUESTION 3 
The ‘Q3_ForestFires’ sheet of your student file contains variables pertaining to forest fire risk factors. These include daily high temperature (°C), relative humidity (%), and wind (km/h). We want to determine if these variables combined can predict a fire risk index (called DMC in the worksheet). 
Use the dataset to perform multiple linear regression modelling in SPSS and answer the following questions. To support your answers to these questions, the following SPSS output tables must be presented: Model Summary, ANOVA, Coefficients. 
(a) What is the multiple correlation coefficient and the coefficient of multiple determination for this model? Explain what each of these terms means, and comment on their value. 
(b) Examine the ANOVA table. State the hypotheses you are testing using the results of this table. 
What can you conclude about the model? 
(c) What is the multiple linear equation for this regression model? Use it to predict the fire risk index value using the following measurements: temperature of 23 °C, relative humidity of 56%, and windspeed of 5 km/hour.  
(d) Which variables are significant predictors of this fire risk index and what impact do they have on the prediction? Explain. 
QUESTION 4 
Researchers are testing a new drug for its efficacy in maintaining stable blood glucose levels over time in Type II Diabetes Mellitus. A set of 20 patients were recruited for this study and prescribed the new drug. Blood glucose levels were tested at 12 weeks and 6 months after commencing treatment. Use SPSS to test whether blood glucose levels were stable over this period of time at the 5% significance level. Assume the data are not normally distributed. To receive full marks, all relevant SPSS outputs must be presented. 
The corresponding data can be found in the ‘Q4_BloodGlucose’ of your data file. 

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