HI6007 Statistics and Research Methods for Business Decision Making
Assessment Details and Submission Guidelines | |
Trimester | T1 2019 |
Unit Code | HI6007 |
Unit Title | Statistics and Research Methods for Business Decision Making |
Assessment Type | Assessment 2 |
Assessment Title | Group Assignment |
Purpose of the assessment (with ULO Mapping) | Students are required to show the understanding of the principles and techniques of business research and statistical analysis taught in the course. |
Weight | 30 % of the total assessments |
Total Marks | 30 |
Word limit | N/A |
Due Date | Lecture 10 |
Submission Guidelines | All work must be submitted on Blackboard by the due date along with a completed Assignment Cover Page.The assignment must be in MS Word format, no spacing, 12-pt Arial font and 2 cm margins on all four sides of your page with appropriate section headings and page numbers.Reference sources must be cited in the text of the report, and listed appropriately at the end in a reference list using Harvard referencing style. |
Assignment Specifications
Purpose:
This assignment aims at Understand various qualitative and quantitative research methodologies and techniques, and other general purposes are:
- Explain how statistical techniques can solve business problems
- Identify and evaluate valid statistical techniques in a given scenario to solve business problems
- Explain and justify the results of a statistical analysis in the context of critical reasoning for a business problem solving
- Apply statistical knowledge to summarize data graphically and statistically, either manually or via a computer package
- Justify and interpret statistical/analytical scenarios that best fits business solution
Assignment Structure should be as the following:
Instructions:
- Your assignment must be submitted in WORD format only!
· When answering questions, wherever required, you should copy/cut and paste the Excel output (e.g., plots, regression output etc.) to show your working/output.
- Submit your assignment through Safe-Assign in the course website, under the Assignments and due dates, Assignment Final Submission before the due date.
- You are required to keep an electronic copy of your submitted assignment to re-submit, in case the original submission is failed and/or you are asked to resubmit.
- Please check your Holmes email prior to reporting your assignment mark regularly for possible communications due to failure in your submission.
Please read below information carefully and respond all questions listed.
Question 1
Australian exports (goods and services) along with its top 8 export markets in 2004-05 and 2014-15 are shown in the table stored in file EXPORTS.XLSX (in the course website). Using this data, answer the questions below. (4 Marks)
- Use an appropriate graphical technique to compare the value of Australian exports (in A$ bn) in 2004-05 and 2014-15, broken down by country of export destination.
(1 mark)
- Use an appropriate graphical technique to compare the percentage value of Australian exports (in %) in 2004-05 and 2014-15, broken down by country of export destination.
(1 mark)
- Comment your observations in parts (a) and (b). (2 marks)
Question 2.
The following data are the 40 days umbrella sales from a store. (8 Marks)
63 | 74 | 42 | 65 | 51 | 54 | 36 | 56 | 68 | 57 |
62 | 64 | 76 | 67 | 79 | 61 | 81 | 77 | 59 | 38 |
84 | 68 | 71 | 94 | 71 | 86 | 69 | 75 | 91 | 55 |
48 | 82 | 83 | 54 | 79 | 62 | 68 | 58 | 41 | 47 |
- Construct a frequency distribution and a relative frequency distribution for the data.
(2 marks)
- Construct a cumulative frequency distribution and a cumulative relative frequency distribution for
the data. (2 marks)
- Plot a relative frequency histogram for the data. (1 mark)
- Construct an ogive for the data. (1 mark)
- What proportion of the grades is less than 60? (1 mark)
Classes Frequency Relative Frequency Cumulative Frequency Cumulative Relative Frequency 30 – 40 40 – 50 50 – 60 60 – 70 70 – 80 80 – 90 90 – 100 |
What proportion of the grades is more than 70? (1mark) Use following class intervals to answer the above questions
Question 3. (18 Marks)
Assume you are a research analyst in an economic consultancy firm. Your team leader has given you a research task to investigate whether the per capita retail turnover in Australia is a good predictor of the final consumption expenditure of the country.
Relevant Variables:
FINAL CONSUMPTION EXPENDITURE: (Trend) in $ Millions; – quarterly data from September 1983 to March 2016.
RETAIL TURNOVER PER CAPITA – (Trend) Total (State) in $;- quarterly data from September 1983 to March 2016.
[Quarterly time series data (for the period September 1983 to March 2016) are from the Australian Bureau of Statistics (ABS)]
The data are stored in the file named “ASSIGNMENTDATA.XLSX” in the course website. Using EXCEL, answer below questions:
- Using an appropriate graphical descriptive measure (relevant for time series data) describe the two variables. (1 mark)
- Use an appropriate plot to investigate the relationship between FINAL CONSUMPTION EXPENDITURE and RETAIL TURNOVER PER CAPITA. Briefly explain the selection of each variable on the X and Y axes and why? (2 marks)
- Prepare a numerical summary report about the data on the two variables by including the summary measures, mean, median, range, variance, standard deviation, coefficient of variation, smallest and largest values, and the three quartiles, for each variable.
(3 marks)
- Calculate the coefficient of correlation (r) between FINAL CONSUMPTION EXPENDITURE and RETAIL TURNOVER PER CAPITA. Then, interpret it.
(2 marks)
- Estimate a simple linear regression model and present the estimated linear equation. Then, interpret the coefficient estimates of the linear model. (4 marks)
- Determine the coefficient of determination R^{2} and interpret it. (2 marks)
- Test whether FINAL CONSUMPTION EXPENDITURE positively and significantly increases with
RETAIL TURNOVER PER CAPITA at the 5% significance level.
(3 marks)
- What is the value of the standard error of the estimate (se). Then, comment on the fitness of the linear regression model? (1 mark)
Marking criteria
Marking criteria | Weighting |
Australian Exports Analysis: Appropriate graphical technique for comparing the valuesAppropriate graphical technique for comparing the percentage valuesComparing a and b | 4 marks |
1 mark | |
1 mark | |
2 marks | |
2. Analysis of sales of a product: | 8 marks 2 marks 2 marks 1 mark markmarks |
a) Appropriate frequency distribution | |
b) Appropriate cumulative frequency distribution | |
c) Histogram | |
d) Ogive | |
e) and f) Proportion | |
3. Estimation and testing significance level: | 18 marks markmarksmarks 2 marks 4 marks marksmarks 1 mark |
a) Descriptive measure | |
b) Scatter plot | |
c) Numerical summary report | |
d) Correlation coefficient | |
e) Estimating regression line | |
f) Estimating coefficient of determination | |
g) Testing significance | |
h) Standard error | |
TOTAL Weight | 30% |
Assessment Feedback to the Student: |
Marking Rubric
Excellent | Very Good | Good | Satisfactory | Unsatisfactory | |
Australian Export | Demonstration of | Demonstration | Demonstration | Demonstration | Demonstration of |
Analysis | outstanding | of very good | of good | of basic | poor knowledge on |
knowledge on | knowledge on | knowledge on | knowledge on | descriptive | |
descriptive | descriptive | descriptive | descriptive | techniques | |
techniques | techniques | techniques | techniques | ||
Analysis of sales of a | Demonstration of | Demonstration | Demonstration | Demonstration | Demonstration of |
product | outstanding | of very good | of good | of basic | poor knowledge on |
knowledge on | knowledge on | knowledge on | knowledge on | descriptive | |
descriptive | descriptive | descriptive | descriptive | measures | |
measures | measures | measures | measures | ||
Estimation and | Demonstration of | Demonstration | Demonstration | Demonstration | Demonstration of |
testing significance | outstanding | of very good | of good | of basic | poor knowledge on |
level | knowledge on correlation and | knowledge on correlation and | knowledge on correlation and | knowledge on correlation and | correlation and regression analysis |
regression analysis | regression | regression | regression | ||
analysis | analysis | analysis |