Constructing A Modified Control Chart,The X-Bar And R Values
Constructing A Modified Control Chart,The X-Bar And R Values
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Problem 1 (25 points)
The x-bar and R values for 20 samples of size five are shown in the following table. Specifications on this product have been established as 0.550 ± 0.02.
Sample Number | Xbar | R |
1 | 0.549 | 0.0025 |
2 | 0.548 | 0.0021 |
3 | 0.548 | 0.0023 |
4 | 0.551 | 0.0029 |
5 | 0.553 | 0.0018 |
6 | 0.552 | 0.0017 |
7 | 0.55 | 0.002 |
8 | 0.551 | 0.0024 |
9 | 0.553 | 0.0022 |
10 | 0.556 | 0.0028 |
11 | 0.547 | 0.002 |
12 | 0.545 | 0.003 |
13 | 0.549 | 0.0031 |
14 | 0.552 | 0.0022 |
15 | 0.55 | 0.0023 |
16 | 0.548 | 0.0021 |
17 | 0.556 | 0.0019 |
18 | 0.546 | 0.0018 |
19 | 0.55 | 0.0021 |
20 | 0.551 | 0.0022 |
- Construct a modified control chart with three sigma limits, assuming that if the true process fraction non conforming is as large as 1%, the process is unacceptable.
- Suppose that if the true process fraction nonconforming is as large as 1%, we would like an acceptance control chart to detect this out of control condition with probability 0.90. Construct this acceptance control chart and compare it to the chart obtained in part
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Data Analysis, R
Data Analysis, R
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Instructions:
The project aims to analyze a real data set. You are free to perform any analyses that you consider to be relevant and informative. Use R to perform calculations and computations. Do not submit pages of raw computer output, but you need include important tables or attach graphs to your report. Write concise answers that clearly describe the steps in your analysis and your conclusions. You will be graded with respect to how well your analyses reveal interesting aspects of the data set, the interpretation of your results, how well you justify your methods of analysis, and the overall quality of your writing.
Your report should contain the following:
- Provide a one paragraph summary of your major findings. This should not contain any formulas or mathematical symbols. It should be well written so that it could be easily understood by anybody else who is not a statistician.
- Provide a description of the steps taken to identify your best model (or models). Do not give all the details of your search. Do not submit any computer output in this section. Simply outline the issues you considered, your decisions, and the sequence of steps you took to develop a model. (Not to exceed one typed page.)
- Provide a description of your best model (or models), including estimates of parameters and their standard errors, and the statistical inference you performed. You may copy small parts of computer output into this part of the report. Discuss and interpret any important features of your model and state your conclusions in the context of the problem. (Not to exceed two typed pages).
- Provide convincing evidence that your analysis is based on a good model. Discussion of residual plots and other diagnostic checks would be appropriate. You may attach graphs but lists of raw computer output should not be submitted and will be ignored. (Not to exceed one typed page, excluding graphs.)
- You may submit one more paragraph outlining additional analyses that you would have done if you had more time. You will earn points for good suggestions and lose points for suggestions with little potential value.
Data set description:
This data set comes from a study conducted by Baty et al. (2006). The original purpose of this study was to measure the influence of beverages on blood gene expression. They would like to explore the underlying mechanisms of the cardio protective effects of beverages (You are not restricted to their study purpose). Six healthy individuals participated in the randomized controlled cross-over experiment. On 4 independent days they had 4 different beverages (500mL each: grape juice, red wine, 40g diluted ethanol, water). Blood samples were taken at baseline (0 hour, without drinking beverages), 1, 2, 4, 12 hours after the drink together with standardized nutrition. RNA of 120 samples was hybridized on Affymetrix microarrays. The gene expression data were obtained for 108 blood samples. The data set is Beverage Study Data Set. The data set is contained in “Alldata.Rdata” file, which can be loaded into your R by using the command (after setting the working directory to the place where you saved the data set) load(file=”Alldata.Rdata”)
Within the data set, “Alldata” is a list, which includes the following objects:
“originaldata” “trt1” “trt2” “trt3” “trt4”
“time_h0” “time_h1” “time_h2” “time_h4” “time_h12”
“ind1” “ind2” “ind3” “ind4” “ind5” “ind6”
The objects included in the data set are
originaldata: All the gene expression data (transformed counts data);
trt1: IDs for individuals participated in Alcohol group;
trt2: IDs for individuals participated in Grape juice group;
trt3: IDs for individuals participated in Red wine group;
trt4: IDs for individuals participated in Water group;
time h0: Observations measured at baseline;
time h1: Observations measured at 1 hour after the drink;
time h2: Observations measured at 2 hours after the drink;
time h4: Observations measured at 4 hours after the drink;
time h12: Observations measured at 12 hours after the drink;
ind1: data obtained from individual 1;
ind2: data obtained from individual 2;
ind3: data obtained from individual 3;
ind4: data obtained from individual 4;
ind5: data obtained from individual 5;
ind6: data obtained from individual 6;
You can access to each object by using the operator $. For example, if you want to
get the data contained in trt1, you could type in the following
Alldata$trt1
The original data is a matrix containing 22283 rows and 130 columns. Each row corresponds to one gene, and the 3rd column to the 110th column correspond to gene expression data. The rest columns are gene IDs or the gene annotation information. The following is very small part of the data
GSM87863 GSM87887 GSM87896 GSM87934 GSM87943 GSM87853
1 6.96959 6.84646 6.99376 7.0678 7.07566 7.18618
2 4.94771 4.63228 4.47609 4.41107 4.6249 4.61241
3 7.38956 7.21881 7.62192 7.76446 7.4327 7.3896
4 7.73394 7.73069 8.12781 7.92782 7.96697 7.96694
5 3.10916 3.3146 3.4218 3.46084 3.29934 3.35648
6 6.93594 7.23465 6.63625 6.72077 7.15 7.07379
The first column in the above data example corresponds to the observation ID GSM87863. This ID is contained in the variable Alldata$trt1, Alldata$time_h0 and Alldata$ind1. This means that this column data (gene expressions) are obtained from the individual 1 at time 0h who participated in the treatment 1 (Alcohol group). Each row corresponds the gene expression for each gene. In the above data set, the rows 1-6 provide gene expressions for the first 6 genes.
References:
Baty F, Facompre M, Wiegand J, Schwager J et al. Analysis with respect to instrumental variables for the exploration of microarray data structures. BMC Bioinformatics, 2006 Sep 29;7:422.
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Inferential Research and Statistics Project
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University of Phoenix Material
Inferential Research and Statistics Project
Part 1
Select one of the following scenarios based on your particular field of interest in psychology:
- General Psychology:
- Clinicians at a small clinic have been introduced to a new method to treat post-traumatic stress disorder (PTSD) in their clients for veterans. Research indicates that virtual reality (VR) is a highly effective treatment option for patients with PTSD. Currently, the clinic uses only cognitive processing therapy (CPT) with their patients suffering from PTSD. The clinicians would like to find out whether VR therapy has different results from CPT therapy. The measure used by the clinic to measure PTSD symptoms is the Combat Exposure Scale. Both therapies need to be applied for a minimum of 12 weeks to be effective.
Write a 525- to 750-word paper that addresses the following for your chosen scenario:
- Clearly define the problem or issue you are addressing. Provide a brief background of any research you have found that might affect your research hypothesis.
- Create a research hypothesis based on the information provided in each scenario. You have been given a data set (Excel document) with two sets of interval data (just the numbers, as you must decide what they represent, such as method A results or method B results). This means you are going to test one thing against another, such as which method works best (step 1 of the steps to hypothesis testing). State the null and research hypotheses. Explain whether these hypotheses require a one-tailed test or two-tailed test, and explain your rationale.
- Describe the sample you will use. Sample size will be 30 for each group, which are provided in your data set. Explain what type of sampling you selected.
- Do you think you would also collect some descriptive data, such as gender, age, or shift? Why do you think it makes sense to collect descriptive data?
Format your paper according to APA guidelines.
Example
You have a hypothesis that two drugs have different effects on lowering anxiety. You would have anxiety scores for drug A and anxiety scores for drug B (all after 4 weeks of treatment) to run inferential analysis for after 4 weeks.
- Null hypothesis is H_{0}: drug A = drug B
- Research hypothesis is H_{1}:_{ d}rug A ≠ drug B
- Dependent variable: Anxiety score changed after treatment.
- Independent variable: drug treatment
Because you did not state a direction in your hypotheses (better than or worse than), this will be a two-tailed test. You are looking for differences in either direction. You would set your alpha level of .05 and have a sample for each group of 30 people that were volunteers for the study.
- Provide the main finding of the study. What did you prove or fail to prove?
- Provide recommendations based on your findings.
Format any citations in your presentation according to APA guidelines.
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Assignment help-Unit: TSTA101 – INTRODUCTORY STATISTICS
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Individual Assignment
Unit: TSTA101 – INTRODUCTORY STATISTICS
Due Date: Thursday, 28/09/2017 (16:00pm)
Number of Questions: Six (6) Questions
Total Marks: Twenty (20) marks
Instructions: All questions should be attempted.
The marks of each question would be awarded based on your understanding of the questions, concepts and procedures; hence you should demonstrate your answers step by step.
Question 1 [3 marks]
Part a)
Find the following probabilities by checking the z table
i) P((Z>-0.8)
ii) P (-1.3<Z<-0.7)
iii) Z0.2
Part b)
A new car has recently hit the market. The distance travelled on 1 gallon of fuel is normally distributed with a mean of 65 miles and a standard deviation of 4 miles. Find the probability of the following events.
i) The car travels more than 70 miles per gallon.
ii) The car travels less than 60 miles per gallon.
iii) The car travels between 55 and 70 miles per gallon.
Question 2 [3 marks]
Part a)
A sample of n=25 observations is drawn from a normal population with μ=100 and σ=20. Find the following.
i) P(<96)
ii) P(96<<105)
Part b)
The amount of time the university professors devote to their jobs per week is normally distributed with a mean of 52 hours and a standard deviation of 6 hours.
i) What is the probability that a professor works for more than 60 hours per weeks?
ii) Find the probability that the mean amount of work per week for three randomly selected professors is more than 60 hours?
Question 3 [2 marks]
Part a)
Given the following information =500, σ=12, n=50
i) Determine the 95% confidence interval estimate of population mean.
ii) Determine the 99% confidence interval estimate of population mean.
Part b)
A statistics practitioner calculated the mean and standard deviation from a sample of 51. They are =120 and s=15.
(i) Estimate the population mean with 95% confidence level.
(ii) Estimate the population mean with 99% confidence level.
X
X
X
X
Question 4 [4 marks]
Part a)
Calculate the statistic, set up the rejection region, interpret the result, and draw the sampling distribution.
H0: μ=10
H1: μ≠10
Given that: σ=10, n=100, =10, α=0.05.
Part b)
A statistics practitioner is in the process of testing to determine whether is enough evidence to infer that the population mean is different from 180. She calculated the mean and standard deviation of a sample of 200 observations as =175 and s=22. Calculate the value of the test statistic of the test required to determine whether there is enough evidence to infer at the 5% significance level that the population mean is different from 180.
Question 5 (2 marks)
Suppose you are using a completely randomized design to study some phenomenon.
There are five treatment levels and a total of 55 people in the study. Each treatment
level has the same sample size. Complete the following ANOVA. Use α=0.05 to find the table F value and use the data to test the null hypothesis.
Source of Variance
SS
df
MS
F
Treatment
583.39
Error
972.18
Total
1555.57
Question 6 (6marks)
There is a simple linear regression model given by:
where price = used car price in dollars and
age = age of the car in years.
The EXCEL results obtained using Ordinary Least Squares are presented below:
Regression Statistics
R2
0.077
Standard Error
42069
Observations
117
Coefficients
Standard Error
t Stat
Intercept
A
6748
7.035
Age
-2658
856
B
Use the above output for answering the following questions:
a) Calculate the missing values from the summary output: A and B
b) Interpret the slope of the regression line.
c) Write down the estimated linear regression line.
d) What is the value of the coefficient of determination? Interpret this value
e) What is the value of the coefficient of correlation? Interpret this value.
f) Test whether the estimated coefficient of Age is significantly less than zero at the 5% level of significance.
g) Predict price if the car has driven 3 years. X
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Computer Homework help-R, Statistical Report
Computer Homework help-R, Statistical Report
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Requirement:
For the computer homework questions, please use the report format:
First, answer the questions in complete sentences, using the output text
or figures as evidence to support your answer; then, attach the program
codes (with comments to code more readable) as an appendix by the
end of the statistical report. When you copy and paste the text part,
please box them and treat them as tables. Please label all your figures
and tables. The code in your appendix may contain extra code you have
used or explored, but should be executable (no errors).
1.Please use R to summarize the Men’s triple jump Olympic records.
- Please report the five number summaries of the jumping distance.
- Please construct a scatter plot with a regression line.
- What are the covariance and correlation between “year” and
“distance”? Please interpret your result in the context.
Year Distance
1896 13.71
1900 14.47
1904 14.35
1908 14.92
1912 14.64
1920 14.50
1924 15.53
1928 15.21
1932 15.72
1936 16.00
1948 15.40
1952 16.22
1956 16.35
1960 16.81
1964 16.85
1968 17.39
1972 17.35
1976 17.29
1980 17.35
1984 17.25
1988 17.61
1992 18.17
1996 18.09
2000 17.71
2004 17.79
2008 17.67
2012 17.81
2016 17.86
- Please use R to summarize Labor Data (please see the file “Labor Data”). You can choose whatever plots and tables to present, as long as they are meaningful. What can you
find from this collected data?
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Computer Homework help-R, Statistical Report
Need help-STAT2112.13
Need help-Statistics Assignment:Geochemistry 1
Need help-Statistics Assignment:Geochemistry 1
Field and Laboratory Techniques in Geochemistry 1
Statistics Assignment
Question 1) 10%
The data for a digestion of Bolivian tailings are provided. The elements are grouped as majors, traces and rare earth elements. Produce, using the descriptive statistics command in Excel (or any other suitable programme), summary data for each of these three groupings (Mean, Standard Error, Median, Mode, Standard Deviation, Sample Variance, Kurtosis, Skewness, Range, Minimum, Maximum, Sum and Count).
The data should be presented in tabulated form.
Question 2) 10%
Explain, using a maximum of three sentences for each, what you understand by the terms: Mean, Standard Error, Median, Mode, Standard Deviation, Sample Variance, Kurtosis, Skewness, Range, Minimum, Maximum, Sum and Count.
Question 3) 10%
Calculate the precision for each element analysed. (Hint the copy and paste tool is very useful for formulae).
Question 4) 10%
The data for a digestion of Bolivian uncontaminated soil are provided. The elements are grouped as majors, traces and rare earth elements. Calculate the mean, standard deviation, standard error and median of the samples for each of these three categories. Comment on any elements which show a large median/mean difference (hint Bi might be worth comparing in this context; for example against a major element). Your answer should incorporate the word ‘outlier’.
Question 5) 10%
BCR-1 and JB-3 are soil CRM materials. They were digested at the same time and using exactly the same methodology as the samples themselves. Calculate the accuracy of the digestion by comparing the results with the given elemental concentrations of the reference materials (BCR-1 rv and JB-3 rv). Comment on the accuracy of the analysis.
Question 6) 25%
The data for chloride concentrations of Regent’s canal and Pennine stream water, as determined by Ion Chromatography (IC), are presented. The data to be analysed are those collected from the Regent’s canal (RC in the spreadsheet itself). To the right of the spreadsheet these data has been extracted to help you answer the following questions.
Question 6a) the samples were analysed at two dilutions: a hundred fold (*100) and neat (*1). Why do you think that such a difference was reported in concentration? Which of these ‘dilutions’ do you trust?
Question 6b) construct a calibration curve (hint, scatter graph). The calibration standards employed were made up to 10, 20, 40 and 60 mg L^{-1}. Plot a suitable regression line and display the R^{2 }value on the graph, from this calculate the Pearson correlation coefficient (hint this is a one-step transformation)
Question 6c) do you think that drift correction might be necessary? Plot a suitable scatter graph to illustrate your answer. Note there is not a definitive yes or no answer to this question. You will be awarded marks on the strength of your reasoning.
Question 6d), using the blank data determine the LOD and LOQ for the complete analytical run. Describe, in a maximum of four sentences, what is meant by the terms LOD and LOQ.
Question 6e) Determine the precision* and accuracy (hint, consider the CRM dilution factor) of the Regent’s canal data. The concentration of chloride in the Battle reference standard is as follows:
*Note there are two duplicate pairs: 1 and 1a together with 2 and 2a. Calculate the individual precisions and the combined overall precision by any appropriate method.
Question 7) 25%
The data provided are from a column experiment which investigated the evolution of pore water concentrations over a modelled twenty year period. The column was packed with uncontaminated Bolivian soil together with sulphide mine tailings.
Question 7a) Produce a correlation matrix encompassing all of the elements (hint spreadsheet 30 gives a suitable method and also remember to remove all non-numerical data).
Question 7b) Produce three scatter graphs from the data. The first should show a strong positive correlation, the second a negative correlation and the third show minimal correlation. For each of these graphs plot a regression line, produce a linear equation and a R^{2} value. From the latter obtain the value of r (Pearson’s correlation).
7c) Calculate a Spearman correlation coefficient for the Zn and Cd concentrations (hint, follow the ranking formulae given in spreadsheet 11).
When comparing the Pearson and Spearman correlation coefficient, which of the two is more sensitive to outliers? Looking at the formulae can you suggest a reason for your conclusion?
Pearson
Spearman
7d) Give an example, not necessarily from the scientific literature, of correlation not implying causation (hint, Wikipedia has a good page addressing this specific question).
Need help-Statistics Assignment:Geochemistry 1
Purchase Assignment-Quantitative Methods for Decision Making
Purchase Assignment-Quantitative Methods for Decision Making
Department of Management and Marketing
Quantitative Methods for Decision Making
Project 1
Word report (the hand writing reports will not be accepted) that includes the following three parts:
Question 1:
An investment company has classified its clients according to their gender and the composition of their investment portfolio (bonds, stocks, or a diversified mix of bonds and stocks). The proportions of clients falling into the various categories are shown in the following table:
Gender |
Portfolio composition | ||
B (Bonds) | S (Stocks) | D (Diversified) | |
M (Male) | 0.18 | 0.20 | 0.25 |
F (Female) | 0.12 | 0.10 | 0.15 |
- What is the probability that a randomly selected client is male and has a diversified portfolio?
- Find the probability that a randomly selected client is male, given that the client has a diversified portfolio?
- Find the probability that a randomly selected client is Female, given that the client has a portfolio is composed of Bonds?
- Let us define the following two events:
E1: the investor is a male
E2: the portfolio is composed of Stocks.
Can we conclude that the events E1 and E2 are independent?
Question 2:
In a hotel chain, the average number of rooms rented daily during each month is 50 rooms. The population of rooms rented daily is assumed to be normally distributed with a standard deviation of 4 rooms. For a specific month of the year, what is the probability that the number of rented rooms, in that month, is between 45 and 60 rooms?
Question 3:
The University registrar office has got some data regarding a sample of 200 students among those enrolled in the MBA Program. The registrar notes that 50 students of the sample are holders of a bachelor degree in business administration. What is the probability that among this sample 20 students hold a bachelor in business administration?
Purchase Assignment-Quantitative Methods for Decision Making
RES5115: RESEARCH PREPARATION: PRINCIPLES AND APPROACHES QUANTITATIVE ASSIGNMENT(SPSS)
RES5115: RESEARCH PREPARATION: PRINCIPLES AND APPROACHES QUANTITATIVE ASSIGNMENT(SPSS)
SCHOOL OF SCIENCE
_______________________________________________________________________________________________
RES5115: RESEARCH PREPARATION: PRINCIPLES AND APPROACHES
QUANTITATIVE ASSIGNMENT
DUE DATE: Friday 28th of October 2016 before midnight
For this assignment, you are expected to perform all analyses using either SPSS or Excel/Real Statistics. However prior to this, you will need to generate your own sub-sample (unique to your student ID) from a larger sample using the random number generator in Microsoft Excel. To do this, you must first activate the Analysis ToolPak in Excel by following steps below.
Step 1: Open a new Excel spreadsheet as shown below.
2
Step 2: Go to File → Options.
Step 3: Click on Add-ins and then click Go…
3
Step 4: Select Analysis ToolPak and click OK.
You can now use the Analysis ToolPak under the Data tab by clicking on Data Analysis.
Note that the above steps are for Microsoft Excel 2010 and may differ slightly if you are using the 2013 version.
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QUESTION 1 [25 MARKS]
BACKGROUND
A study was conducted to investigate the effects of short-term treatments with growth hormone (GH) on biochemical markers of bone metabolism in men with idiopathic osteoporosis. Subjects ranged in age from 32 to 57 years. Among the data collected were serum concentrations of insulin-like growth factor binding protein-3 at 0 and 7 days after the first injection and 1, 4, 8 and 12 weeks after the last injection (i.e. post- treatment) with GH. The serum concentration data for 116 men are given in the Excel file “Serum.xlsx”.
TASK 1 – SELECTING A SUB-SAMPLE
Open the Serum.xlsx data file as shown.
Click on the “Data” tab and then run the “Data Analysis” tool pack.
5
Select Random Number Generation and click OK.
Select “Uniform” in the drop-down menu next to “Distribution”. Then fill out the other boxes as shown. In the box corresponding to “Random Seed”, make sure you type the last two digits of your student ID here. This will ensure the set of random numbers is unique to you (unless someone else shares the same two numbers).
You should now be able to see 116 random numbers in Column H. Take note that your numbers will be different to those shown here.
Last two digits only
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Highlight all the data in from Columns A to H using the mouse/keyboard. Then click on and select Custom Sort…
Now click on next to ColumnSort by and select (Column H). Make sure that under Order, it is set to Smallest to Largest. Then click OK.
The data are now sorted according to Column H. Again, note that what is shown here is very likely to be different to what you will have. Now, select the first 60 observations (i.e. Rows 2 to 61) from Columns A to G and copy this sub-sample across to SPSS or another Excel spreasheet (e.g. Sheet 2). These observations form the dataset that you will be working with for this question.
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TASK 2 – ANALYSING THE DATA
(i) Use SPSS or Excel/Real Statistics and generate the necessary summary statistics and figures to describe the serum concentrations 0 and 7 days after the first GH injection. Proper interpretation of these output in the context of the problem is expected. (ii) Use the appropriate test in SPSS or Excel/Real Statistics and determine whether the GH treatment had any significant impact on serum concentration 7 days after the 1st injection. You will need to comment on the nature and extent of these differences (if any). Hypothesis statements are not necessary. (iii) Confidence intervals should be presented and interpreted whenever possible. (iv) All relevant assumptions associated with your chosen test must be verified. (v) Repeat steps (i) – (iv), but in this instance compare the serum concentrations 1, 4, 8 and 12 weeks after the last GH injection. If the initial analysis suggests a difference, you will then need to perform a post hoc test to determine where the difference(s) lie. Hypothesis statements are not necessary. (vi) Based on the outcomes of the two analyses, comment on the short-term and post-treatment effect of GH treatment on serum concentrations.
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QUESTION 2 [25 MARKS]
BACKGROUND
Mental illness within the Australian population is becoming more apparent. A nationwide survey in 2007 on mental health and wellbeing conducted by the Australian Bureau of Statistics (ABS) found that an estimated 3.2 million Australian (20% of the population between the ages of 16 and 85) had a mental disorder in the twelve months prior to the survey, and the estimated economic impact of mental health problems is up to $20 billion each year.
The mental health of fly-in/fly-out (FIFO) workers in resources sector became a subject of interest in recent years. Although the financial reward is great, reports of FIFO practices negatively impacting the workers and their Australian families are not uncommon.
To determine the extent of the mental health problem in the resources industry, a health and well-being survey was carried at a particular mining company. In the survey are questions relating to the worker’s demographics, behaviour in the past twelve months, work/lifestyle/family-related issues and others.
Also included in the survey are the Kessler’s mental health questions which will allow one to clinically determine the severity of mental health problems for an individual. The Kessler questions were developed by Professor Kessler and Mroczek (1992) to assess the severity (“1 = None of the time”, “2 = A little of the time”, “3 = Some of the time”, “4 = Most of the time” or “5 = All of the time”) of anxiety and depression related symptoms experienced in the past month. Collectively, they are the Kessler 10 or K10 questions. A K10 score is calculated by summing the scores of the 10 questions. The minimum possible score is 10 (low distress) and the maximum being 50 (very high distress).
Suppose that you are interested in examining whether there is an association between worker’s behaviours and their distress levels in the past 12 months. The items of interest are presented in Table 1 and the relevant survey data are given in the Excel file “Mental Health.xlsx”. Note that a blank entry represents a missing response.
Table 1 Items of interest in the Health and Well-Being questionnaire Over the past 12 month (1 = Daily; 2 = Weekly; 3 = Monthly; 4 = Once or Twice; 5 = Almost never) (1) How often have you felt worn out? (2) How often have you been emotionally drained? (3) How often have you been irritable? (4) How often have you had aches and pains? (5) How often have you argued with a work colleague? (6) How often have you felt anxious? (7) How often have you had no energy or enthusiasm? (8) How often have you had 6 or more standard drink in one session? (9) How often have you used prescription or non-prescription drugs? (10) How often have you missed a meal? (11) How often have you argued with friends or family? (12) How often have you had trouble sleeping? (13) How often have you exercised?
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TASK 1 – SELECTING A SUB-SAMPLE
Open the Mental Health.xlsx data file as shown.
Click on the “Data” tab and then run the Analysis ToolPak by clicking on Data Analysis.
Select Random Number Generation and click OK.
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Select “Uniform” in the drop-down menu next to “Distribution”. Then fill out the other boxes as shown. In the box corresponding to “Random Seed”, make sure you type the last two digits of your student ID here. This will ensure the set of random numbers is unique to you (unless someone else shares the same two numbers).
You should now be able to see 1269 random numbers in Column O. Take note that your numbers will be different to those shown here.
Last two digits only
11
Highlight all the data in from Columns A to O using the mouse/keyboard. Then click on and select Custom Sort…
Now click on next to Column → Sort by and select (Column O). Then click OK.
The data are now sorted according to Column O. Again, note that what is shown here (next page) is very likely to be different to what you will have.
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Now, select the first 150 observations (i.e. Rows 1 to 151) from Columns A to N. Copy this sub-sample across to another spreadsheet (e.g. Sheet 2) in Excel. In the example below, Sheet 2 was renamed to Sub- sample.
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TASK 2 – SELECTING ITEMS FROM THE QUESTIONNAIRE TO ANALYSE
Now that you have your sub-sample, here you will determine which THREE (3) of the 13 items to analyse. Again, go to the “Data” tab and then run the “Data Analysis” tool pack. Select “Random Number Generation” and click “OK”.
Now generate 13 random uniform numbers and store them in Column P of the sub-sample spreadsheet. In the “Random Seed” box, type the last two digits of your student ID again.
You should now be able to see 13 random numbers in Column P. Take note that your numbers will again be different to those shown here. Now, type the numbers 1 – 13 (to represent the items) in Column Q and right next to the random numbers that you have just generated.
Last two digits only
14
Now, highlight all the numbers in Columns P and Q using the mouse/keyboard. Then sort the numbers by
clicking and then Sort Smallest to Largest.
The first three numbers in Column Q refer to the items in Table 1 that you will need to analyse. In this example, these are items (1), (3) and (13). Copy the relevant data for the 3 items and the corresponding K10 scores across to SPSS or another Excel spreadsheet (e.g. Sheet 3). These observations form the dataset that you will be working with for this question.
TASK 3 – ANALYSING THE DATA
(i) Use SPSS or Excel/Real Statistics and generate all necessary summary statistics and figures to describe the relationship between the workers’ mental health (according to the K10 scores) and each of your selected items (make sure you consider each item separately from the others). Proper interpretation of these output in the context of the problem is expected. (ii) Use the appropriate test in SPSS or Excel/Real Statistics and determine whether there is a difference in distress levels across the response categories for these each of these items. If so, perform a post-hoc test to determine where the difference(s) lie. You will need to comment on the nature and extent of these differences (if any). Hypothesis statements are not necessary. (iii) Confidence intervals should be presented and interpreted whenever possible. (iv) All relevant assumptions associated with your chosen test must be verified. (v) Briefly comment on the practical implications of your findings and how they may be useful.
15
SUBMISSION GUIDELINES:
This assignment must be completed using either SPSS or Excel/Real Statistics. It is expected that your assignment solutions are presented in the context of the problem. Simply stating the numbers from the output table will not suffice. Examples of this are given on Blackboard in the Quantitative weekly folders corresponding to the workshop weeks. The write-up of your analyses must be presented in Microsoft Word in Times New Roman format with 11-pt font with single spacing and in grammatically correct English. The page margins must be 2 cm for all sides. Failure to do so will result in deduction of marks. Furthermore, no handwritten assignments will be accepted. Copy only the relevant outputs, tables and/or figures from SPSS or Excel across to the Word document. All tables and figures must be labelled appropriately. The write-up for each question (not each part) must not exceed THREE (3) pages (including all tables and figures). Any lines exceeding the three-page limit will not be considered or read. Your assignment submission will also need to include the dataset that is unique to your student ID, along with a cover page (see page 16).
You are reminded of the declaration
“I certify that the attached assignment is my own work and that any material drawn from other sources has been acknowledged”.
that you sign when you complete the coversheet. The assignment solutions must be your own work.
You must submit your completed assignment via Turnitin by Friday 28th of October 2016 before midnight. In the interests of fairness, submission extensions will be given only in exceptional circumstances, and then only in accordance with University rules (see page 17).
Note: If your datasets do not match the ones generated with your student ID (last two digits only), you will be awarded a mark of zero regardless of whether the analysis is correct.
SUBMISSION CHECKLIST:
Your submission will need to be a single document containing the following:
o Cover sheet (1 page) o Your solutions to Questions 1 and 2 (maximum of 3 pages per question). o Your datasets (No restriction)
MARKING GUIDELINES:
(1) Adherence to submission guidelines above (10%) (2) Correct selection and implementation of tests and with correct findings (30%) (3) Proper presentation and interpretation of results in the context of the study and objective(s) (30%) (4) Use of statistics to support and re-inforce findings and arguments (15%) (5) Spelling, grammar and overall narrative (15%)
The above marking guidelines are estimates and variations may occur. If a test is incorrectly chosen, then only a maximum of 10 marks is attainable.
16
ASSIGNMENT COVER SHEET Electronic or manual submission
Form: SSC-115-06-08
UNIT CODE: TITLE:
NAME OF STUDENT (PRINT CLEARLY) FAMILY NAME FIRST NAME
STUDENT ID. NO.
NAME OF LECTURER (PRINT CLEARLY)
DUE DATE
Topic of assignment
Group or tutorial (if applicable)
Course
Campus
I certify that the attached assignment is my own work and that any material drawn from other sources has been acknowledged. Copyright in assignments remains my property. I grant permission to the University to make copies of assignments for assessment, review and/or record keeping purposes. I note that the University reserves the right to check my assignment for plagiarism. Should the reproduction of all or part of an assignment be required by the University for any purpose other than those mentioned above, appropriate authorisation will be sought from me on the relevant form.
OFFICE USE ONLY
If handing in an assignment in a paper or other physical form, sign here to indicate that you have read this form, filled it in completely and that you certify as above.
Signature Date
OR, if submitting this paper electronically as per instructions for the unit, place an ‘X’ in the box below to indicate that you have read this form and filled it in completely and that you certify as above. Please include this page in/with your submission. Any electronic responses to this submission will be sent to your ECU email address. Agreement Date
PROCEDURES AND PENALTIES ON LATE ASSIGNMENTS (University Rule 39) A student who wishes to defer the submission of an assignment must apply to the lecturer in charge of the relevant unit or course for an extension of the time within which to submit the assignment. (39.1) Where an extension is sought for the submission of an assignment the application must: be in writing – preferably before the due date; and set out the grounds on which deferral is sought. ( see 39.2) Assignments submitted after the normal or extended date without approval shall incur a penalty of loss of marks. (see 39.5) ACADEMIC MISCONDUCT (University Rule 40) All forms of cheating, plagiarism or collusion are regarded seriously and could result in penalties including loss of marks, exclusion from the unit or cancellation of enrolment. – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
ASSIGNMENT RECEIPT To be completed by the student if the receipt is required
UNIT
NAME OF STUDENT
STUDENT ID. NO.
NAME OF LECTURER
RECEIVED BY
Topic of assignment
DATE RECEIVED
17
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; Misreading the deadline.
All extension requests will require students to complete the Assignment Extension Form (downloadable from the Quantitative Assignment folder on BB), along with appropriate documentation such as a medical certificate, letter from employer, etc. Original copies of these documents will then need to be provided. Scanned or photocopied documents are NOT acceptable. All documents must be provided no longer than ONE week after the assignment deadline.
RES5115: RESEARCH PREPARATION: PRINCIPLES AND APPROACHES QUANTITATIVE ASSIGNMENT(SPSS)
1
SCHOOL OF SCIENCE
_______________________________________________________________________________________________
RES5115: RESEARCH PREPARATION: PRINCIPLES AND APPROACHES
QUANTITATIVE ASSIGNMENT
DUE DATE: Friday 28th of October 2016 before midnight
For this assignment, you are expected to perform all analyses using either SPSS or Excel/Real Statistics. However prior to this, you will need to generate your own sub-sample (unique to your student ID) from a larger sample using the random number generator in Microsoft Excel. To do this, you must first activate the Analysis ToolPak in Excel by following steps below.
Step 1: Open a new Excel spreadsheet as shown below.
2
Step 2: Go to File → Options.
Step 3: Click on Add-ins and then click Go…
3
Step 4: Select Analysis ToolPak and click OK.
You can now use the Analysis ToolPak under the Data tab by clicking on Data Analysis.
Note that the above steps are for Microsoft Excel 2010 and may differ slightly if you are using the 2013 version.
4
QUESTION 1 [25 MARKS]
BACKGROUND
A study was conducted to investigate the effects of short-term treatments with growth hormone (GH) on biochemical markers of bone metabolism in men with idiopathic osteoporosis. Subjects ranged in age from 32 to 57 years. Among the data collected were serum concentrations of insulin-like growth factor binding protein-3 at 0 and 7 days after the first injection and 1, 4, 8 and 12 weeks after the last injection (i.e. post- treatment) with GH. The serum concentration data for 116 men are given in the Excel file “Serum.xlsx”.
TASK 1 – SELECTING A SUB-SAMPLE
Open the Serum.xlsx data file as shown.
Click on the “Data” tab and then run the “Data Analysis” tool pack.
5
Select Random Number Generation and click OK.
Select “Uniform” in the drop-down menu next to “Distribution”. Then fill out the other boxes as shown. In the box corresponding to “Random Seed”, make sure you type the last two digits of your student ID here. This will ensure the set of random numbers is unique to you (unless someone else shares the same two numbers).
You should now be able to see 116 random numbers in Column H. Take note that your numbers will be different to those shown here.
Last two digits only
6
Highlight all the data in from Columns A to H using the mouse/keyboard. Then click on and select Custom Sort…
Now click on next to ColumnSort by and select (Column H). Make sure that under Order, it is set to Smallest to Largest. Then click OK.
The data are now sorted according to Column H. Again, note that what is shown here is very likely to be different to what you will have. Now, select the first 60 observations (i.e. Rows 2 to 61) from Columns A to G and copy this sub-sample across to SPSS or another Excel spreasheet (e.g. Sheet 2). These observations form the dataset that you will be working with for this question.
7
TASK 2 – ANALYSING THE DATA
(i) Use SPSS or Excel/Real Statistics and generate the necessary summary statistics and figures to describe the serum concentrations 0 and 7 days after the first GH injection. Proper interpretation of these output in the context of the problem is expected. (ii) Use the appropriate test in SPSS or Excel/Real Statistics and determine whether the GH treatment had any significant impact on serum concentration 7 days after the 1st injection. You will need to comment on the nature and extent of these differences (if any). Hypothesis statements are not necessary. (iii) Confidence intervals should be presented and interpreted whenever possible. (iv) All relevant assumptions associated with your chosen test must be verified. (v) Repeat steps (i) – (iv), but in this instance compare the serum concentrations 1, 4, 8 and 12 weeks after the last GH injection. If the initial analysis suggests a difference, you will then need to perform a post hoc test to determine where the difference(s) lie. Hypothesis statements are not necessary. (vi) Based on the outcomes of the two analyses, comment on the short-term and post-treatment effect of GH treatment on serum concentrations.
8
QUESTION 2 [25 MARKS]
BACKGROUND
Mental illness within the Australian population is becoming more apparent. A nationwide survey in 2007 on mental health and wellbeing conducted by the Australian Bureau of Statistics (ABS) found that an estimated 3.2 million Australian (20% of the population between the ages of 16 and 85) had a mental disorder in the twelve months prior to the survey, and the estimated economic impact of mental health problems is up to $20 billion each year.
The mental health of fly-in/fly-out (FIFO) workers in resources sector became a subject of interest in recent years. Although the financial reward is great, reports of FIFO practices negatively impacting the workers and their Australian families are not uncommon.
To determine the extent of the mental health problem in the resources industry, a health and well-being survey was carried at a particular mining company. In the survey are questions relating to the worker’s demographics, behaviour in the past twelve months, work/lifestyle/family-related issues and others.
Also included in the survey are the Kessler’s mental health questions which will allow one to clinically determine the severity of mental health problems for an individual. The Kessler questions were developed by Professor Kessler and Mroczek (1992) to assess the severity (“1 = None of the time”, “2 = A little of the time”, “3 = Some of the time”, “4 = Most of the time” or “5 = All of the time”) of anxiety and depression related symptoms experienced in the past month. Collectively, they are the Kessler 10 or K10 questions. A K10 score is calculated by summing the scores of the 10 questions. The minimum possible score is 10 (low distress) and the maximum being 50 (very high distress).
Suppose that you are interested in examining whether there is an association between worker’s behaviours and their distress levels in the past 12 months. The items of interest are presented in Table 1 and the relevant survey data are given in the Excel file “Mental Health.xlsx”. Note that a blank entry represents a missing response.
Table 1 Items of interest in the Health and Well-Being questionnaire Over the past 12 month (1 = Daily; 2 = Weekly; 3 = Monthly; 4 = Once or Twice; 5 = Almost never) (1) How often have you felt worn out? (2) How often have you been emotionally drained? (3) How often have you been irritable? (4) How often have you had aches and pains? (5) How often have you argued with a work colleague? (6) How often have you felt anxious? (7) How often have you had no energy or enthusiasm? (8) How often have you had 6 or more standard drink in one session? (9) How often have you used prescription or non-prescription drugs? (10) How often have you missed a meal? (11) How often have you argued with friends or family? (12) How often have you had trouble sleeping? (13) How often have you exercised?
9
TASK 1 – SELECTING A SUB-SAMPLE
Open the Mental Health.xlsx data file as shown.
Click on the “Data” tab and then run the Analysis ToolPak by clicking on Data Analysis.
Select Random Number Generation and click OK.
10
Select “Uniform” in the drop-down menu next to “Distribution”. Then fill out the other boxes as shown. In the box corresponding to “Random Seed”, make sure you type the last two digits of your student ID here. This will ensure the set of random numbers is unique to you (unless someone else shares the same two numbers).
You should now be able to see 1269 random numbers in Column O. Take note that your numbers will be different to those shown here.
Last two digits only
11
Highlight all the data in from Columns A to O using the mouse/keyboard. Then click on and select Custom Sort…
Now click on next to Column → Sort by and select (Column O). Then click OK.
The data are now sorted according to Column O. Again, note that what is shown here (next page) is very likely to be different to what you will have.
12
Now, select the first 150 observations (i.e. Rows 1 to 151) from Columns A to N. Copy this sub-sample across to another spreadsheet (e.g. Sheet 2) in Excel. In the example below, Sheet 2 was renamed to Sub- sample.
13
TASK 2 – SELECTING ITEMS FROM THE QUESTIONNAIRE TO ANALYSE
Now that you have your sub-sample, here you will determine which THREE (3) of the 13 items to analyse. Again, go to the “Data” tab and then run the “Data Analysis” tool pack. Select “Random Number Generation” and click “OK”.
Now generate 13 random uniform numbers and store them in Column P of the sub-sample spreadsheet. In the “Random Seed” box, type the last two digits of your student ID again.
You should now be able to see 13 random numbers in Column P. Take note that your numbers will again be different to those shown here. Now, type the numbers 1 – 13 (to represent the items) in Column Q and right next to the random numbers that you have just generated.
Last two digits only
14
Now, highlight all the numbers in Columns P and Q using the mouse/keyboard. Then sort the numbers by
clicking and then Sort Smallest to Largest.
The first three numbers in Column Q refer to the items in Table 1 that you will need to analyse. In this example, these are items (1), (3) and (13). Copy the relevant data for the 3 items and the corresponding K10 scores across to SPSS or another Excel spreadsheet (e.g. Sheet 3). These observations form the dataset that you will be working with for this question.
TASK 3 – ANALYSING THE DATA
(i) Use SPSS or Excel/Real Statistics and generate all necessary summary statistics and figures to describe the relationship between the workers’ mental health (according to the K10 scores) and each of your selected items (make sure you consider each item separately from the others). Proper interpretation of these output in the context of the problem is expected. (ii) Use the appropriate test in SPSS or Excel/Real Statistics and determine whether there is a difference in distress levels across the response categories for these each of these items. If so, perform a post-hoc test to determine where the difference(s) lie. You will need to comment on the nature and extent of these differences (if any). Hypothesis statements are not necessary. (iii) Confidence intervals should be presented and interpreted whenever possible. (iv) All relevant assumptions associated with your chosen test must be verified. (v) Briefly comment on the practical implications of your findings and how they may be useful.
15
SUBMISSION GUIDELINES:
This assignment must be completed using either SPSS or Excel/Real Statistics. It is expected that your assignment solutions are presented in the context of the problem. Simply stating the numbers from the output table will not suffice. Examples of this are given on Blackboard in the Quantitative weekly folders corresponding to the workshop weeks. The write-up of your analyses must be presented in Microsoft Word in Times New Roman format with 11-pt font with single spacing and in grammatically correct English. The page margins must be 2 cm for all sides. Failure to do so will result in deduction of marks. Furthermore, no handwritten assignments will be accepted. Copy only the relevant outputs, tables and/or figures from SPSS or Excel across to the Word document. All tables and figures must be labelled appropriately. The write-up for each question (not each part) must not exceed THREE (3) pages (including all tables and figures). Any lines exceeding the three-page limit will not be considered or read. Your assignment submission will also need to include the dataset that is unique to your student ID, along with a cover page (see page 16).
You are reminded of the declaration
“I certify that the attached assignment is my own work and that any material drawn from other sources has been acknowledged”.
that you sign when you complete the coversheet. The assignment solutions must be your own work.
You must submit your completed assignment via Turnitin by Friday 28th of October 2016 before midnight. In the interests of fairness, submission extensions will be given only in exceptional circumstances, and then only in accordance with University rules (see page 17).
Note: If your datasets do not match the ones generated with your student ID (last two digits only), you will be awarded a mark of zero regardless of whether the analysis is correct.
SUBMISSION CHECKLIST:
Your submission will need to be a single document containing the following:
o Cover sheet (1 page) o Your solutions to Questions 1 and 2 (maximum of 3 pages per question). o Your datasets (No restriction)
MARKING GUIDELINES:
(1) Adherence to submission guidelines above (10%) (2) Correct selection and implementation of tests and with correct findings (30%) (3) Proper presentation and interpretation of results in the context of the study and objective(s) (30%) (4) Use of statistics to support and re-inforce findings and arguments (15%) (5) Spelling, grammar and overall narrative (15%)
The above marking guidelines are estimates and variations may occur. If a test is incorrectly chosen, then only a maximum of 10 marks is attainable.
16
ASSIGNMENT COVER SHEET Electronic or manual submission
Form: SSC-115-06-08
UNIT CODE: TITLE:
NAME OF STUDENT (PRINT CLEARLY) FAMILY NAME FIRST NAME
STUDENT ID. NO.
NAME OF LECTURER (PRINT CLEARLY)
DUE DATE
Topic of assignment
Group or tutorial (if applicable)
Course
Campus
I certify that the attached assignment is my own work and that any material drawn from other sources has been acknowledged. Copyright in assignments remains my property. I grant permission to the University to make copies of assignments for assessment, review and/or record keeping purposes. I note that the University reserves the right to check my assignment for plagiarism. Should the reproduction of all or part of an assignment be required by the University for any purpose other than those mentioned above, appropriate authorisation will be sought from me on the relevant form.
OFFICE USE ONLY
If handing in an assignment in a paper or other physical form, sign here to indicate that you have read this form, filled it in completely and that you certify as above.
Signature Date
OR, if submitting this paper electronically as per instructions for the unit, place an ‘X’ in the box below to indicate that you have read this form and filled it in completely and that you certify as above. Please include this page in/with your submission. Any electronic responses to this submission will be sent to your ECU email address. Agreement Date
PROCEDURES AND PENALTIES ON LATE ASSIGNMENTS (University Rule 39) A student who wishes to defer the submission of an assignment must apply to the lecturer in charge of the relevant unit or course for an extension of the time within which to submit the assignment. (39.1) Where an extension is sought for the submission of an assignment the application must: be in writing – preferably before the due date; and set out the grounds on which deferral is sought. ( see 39.2) Assignments submitted after the normal or extended date without approval shall incur a penalty of loss of marks. (see 39.5) ACADEMIC MISCONDUCT (University Rule 40) All forms of cheating, plagiarism or collusion are regarded seriously and could result in penalties including loss of marks, exclusion from the unit or cancellation of enrolment. – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
ASSIGNMENT RECEIPT To be completed by the student if the receipt is required
UNIT
NAME OF STUDENT
STUDENT ID. NO.
NAME OF LECTURER
RECEIVED BY
Topic of assignment
DATE RECEIVED
17
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; Misreading the deadline.
All extension requests will require students to complete the Assignment Extension Form (downloadable from the Quantitative Assignment folder on BB), along with appropriate documentation such as a medical certificate, letter from employer, etc. Original copies of these documents will then need to be provided. Scanned or photocopied documents are NOT acceptable. All documents must be provided no longer than ONE week after the assignment deadline.