HI6007 Unit Title Statistics for Business Decisions Assessment Type Assessment 2 Assessment Title Group Assignment

Assessment Details and Submission Guidelines
Trimester T2 2020
Unit Code HI6007
Unit Title Statistics for Business Decisions
Assessment Type Assessment 2
Assessment Title Group Assignment
Purpose of the assessment (with ULO Mapping) Students are required to show understanding of the principles and techniques of business research and statistical analysis taught in the course.
Weight 40% of the total assessments
Total Marks 40
Word limit N/A
Due Date Week 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 only, 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.
HI6007 STATISTICS FOR BUSINESS DECISIONS GROUP ASSIGNMENT
Assignment Specifications
Purpose:
This assignment aims at assessing students’ understanding of different qualitative and quantitative research methodologies and techniques. Other purposes are:
1. Explain how statistical techniques can solve business problems
2. Identify and evaluate valid statistical techniques in a given scenario to solve business problems
3. Explain and justify the results of a statistical analysis in the context of critical reasoning for a business problem solving
4. Apply statistical knowledge to summarize data graphically and statistically, either manually or via a computer package
5. Justify and interpret statistical/analytical scenarios that best fit business solution
Assignment Structure should be as the following:
This is an applied assignment. Students have to show that they understand the principles and techniques taught in this course. Therefore, students are expected to show all the workings, and all problems must be completed in the format taught in class, the lecture notes or prescribed text book. Any problems not done in the prescribed format will not be marked, regardless of the ultimate correctness of the answer.
(Note: The questions and the necessary data are provided under “Assignment and Due date” in the Blackboard.)
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. Otherwise, you will not receive the allocated marks.
• 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.
Important Notice:
All assignments submitted undergo plagiarism checking; if found to have cheated, all involving submissions would receive a mark of zero for this assessment item.
Answer all Questions Question 1 (05 Marks)
A group of researchers conducted a research in order to assess their opinion on expected 20% increase in development tax with the expectation of commencement of a new rail road project. Each person being interviewed was asked whether they would vote for this new change or not. Possible responses were vote for, vote against, and no opinion. 295 respondents said they would vote for the law, 672 said they would vote against the law, and 51 said they had no opinion.
a. Do the responses for this question provide categorical or quantitative data? What is the scale of measurement? (2 marks)
b. Draw a suitable graph and explain whether the results indicate general support for or against increasing the development tax to commence the new rail road project? (3 marks)
Question 2 (10 Marks)
ABZ research consultancy firm conducted a study of how chief executive officers (CEOs) spend their day. The study found that CEOs spend on average about 18 hours per week in meetings, not including conference calls, business meals, and public events. Shown below is the time spent per week in meeting (hours) for a sample of 25 CEOs.
14 15 18 23 15
19 20 13 15 23
23 21 15 20 21
16 15 18 18 19
19 22 23 21 12
a. Prepare a numerical summary report including the summary measures, mean, median, range, variance, standard deviation, and coefficient of variation, smallest and largest values, and the
three quartiles. (2 marks)
b. Use a class width of 2 hours to prepare a frequency distribution and a percentage frequency distribution for the data. (4 marks) c. Prepare a histogram and comment in the shape of the distribution. (4 marks)
Question 3 (10 marks)
Three group of researchers would like to seek your help to determine the methods of data collection and methods of sampling for the following statistical analysis. Propose the suitable method of data collection and method of sampling for each of the following with sufficient justification why you recommend your selection, over other possible methods.
a. Analyse the voting intention of Australian voters for upcoming election.
b. Investigation of reasons for not Big 4 banks (NAB, ANZ, CBA and WBC) passing on the full interest cuts introduced by reserve bank of Australia to its borrowers.
c. Understand the demographic profile of the community living in Hume city council, Melbourne
d. Examine opinions from adults on legalising marijuana use in Australia.
e. Estimation of the average age of children in city of Melbourne.
Question 4 (15 marks)
A sample of 15, 10 years -old children was taken to study whether watching television reduces the amount of physical exercise, causing weight gains. The number of kilograms each child was overweight by was recorded (a negative number indicates the child is underweight). In addition, the number of hours of television viewing per week was also recorded. These data are listed in the table below.
Television(hours) 42 34 25 35 37 38 31 33 19 29 38 28 29 36 18
Overweight (Kg) 8 3 0 0 6 6 3 3 -4 4 4 2 1 6 -3
a. Use an appropriate plot to investigate the relationship between Television(hours) and Overweight
(KG). Briefly explain the selection of each variable on the X and Y axes and why? (3 marks)
b. Calculate and interpret the coefficient of correlation (r) between
Television(hours) and Overweight (KG). (2 marks)
c. Estimate a simple linear regression model and present the estimated linear equation. Then, interpret the coefficient estimates of the linear model. (3 marks)
d. Determine the coefficient of determination (R2) and interpret it. (2 marks)
e. Test the significance of the relationship at the 5% significance level. (3 marks)
f. What is the value of the standard error of the estimate (se). Then, comment on the fitness of the
linear regression model? (2 marks)
Note: (Answer for question (b) to (f) should be supported with the excel output. Hence, you are required to provide the excel output as the supplementary file(s) in Appendix Section.
Marking criteria
Marking criteria Weighting
Question 1
a. Understanding the data type and scale of measurements
b. Appropriate graphical technique to present the survey results and review of the summarized data. 5 marks
2 marks
3 marks
Question 2
a. Understanding descriptive statistics
b. Calculating frequency distribution
c. Drawing histogram and analysing the shape of the histogram 10 marks
2 marks
4 marks
4 marks
Question 3
Understanding methods of data collection and method of sampling 10 marks
Question 4
a. Choosing dependent and independent variable correctly and presenting the relationship
b. Calculating correlation and interpreting the value
c. Estimating regression equation and interpreting slope and intercept coefficient
d. Estimating coefficient of determination and interpreting values. 15 marks
3 marks
2 marks
3 marks
2 marks
e. Testing the significance of the relationship between Dependent and independent variable of the model.
f. Calculating standard error of the model and commenting on fitness of the regression model. 3 marks
2 marks
TOTAL Weight 40 Marks
Assessment Feedback to the Student:
Marking Rubric
Excellent Very Good Good Satisfactory Unsatisfactory
Question 1 a. Understanding the
data type and scale of measurements
Demonstration of outstanding knowledge on data types and scale of measurements Demonstration of very good knowledge on data types and scale of measurements Demonstration of good knowledge on data types and scale of
measurements Demonstration of basic knowledge on data types and scale of measurements Demonstration of poor knowledge on data types and scale of measurements
b. Appropriate graphical
technique to present the survey results and review of the summarized data. Demonstration of outstanding knowledge on graphical techniques and critical review of summarised data
Demonstration of very good knowledge on graphical techniques and critical review of
summarised data
Demonstration of
good knowledge graphical techniques and critical review of
summarised data
Demonstration of basic knowledge on
graphical techniques and critical review of
summarised data
Demonstration of poor knowledge on graphical techniques and critical review of summarised data
Question 2 a. Understanding
descriptive statistics Demonstration of outstanding knowledge on descriptive measures
Demonstration of very good knowledge on descriptive measures Demonstration of good knowledge on descriptive measures
Demonstration of basic knowledge on descriptive measures
Demonstration of poor knowledge on descriptive measures
b Calculating frequency
distribution.
Demonstration of outstanding knowledge on frequency calculation. Demonstration of very good knowledge on
frequency calculation.
Demonstration of good knowledge on frequency calculation. Demonstration of basic knowledge on frequency calculation.
Demonstration of poor knowledge on frequency calculation.
c. Drawing histogram
and analysing the shape
of the histogram
Demonstration of outstanding knowledge on presentation of data using histogram and review of the shape of histogram Demonstration of very good knowledge on presentation of data using histogram and review of the shape of histogram Demonstration of good knowledge on presentation of data using histogram and review of the shape of histogram Demonstration of basic knowledge on presentation of data using histogram and review of the shape of histogram Demonstration of poor knowledge on presentation of data using histogram and review of the shape of histogram
Question 3 Understanding methods of data collection and method of sampling Demonstration of outstanding knowledge on methods of data collection and
method of sampling Demonstration of very good knowledge on methods of data collection and method of
sampling Demonstration of good knowledge on methods of data collection and method of
sampling Demonstration of basic knowledge on methods of data collection and method of
sampling Demonstration of poor knowledge on methods of data collection and method of sampling
Question 4 a. Choosing dependent and independent variable correctly and presenting the relationship.
Demonstration of outstanding knowledge on variable selection and presenting the relationship with suitable chart. Demonstration of very good knowledge on variable selection and presenting the relationship with suitable chart. Demonstration of good knowledge on variable selection and presenting the relationship with suitable chart. Demonstration of basic knowledge on variable selection and presenting the relationship with suitable chart. Demonstration of poor knowledge on variable selection and presenting the relationship with suitable chart.
b. Calculating correlation and interpreting the value
Demonstration of outstanding knowledge on correlation coefficient calculation and interpretation of relationship between
variables Demonstration of very good knowledge on
correlation
coefficient
calculation and interpretation of relationship between variables Demonstration of good knowledge on
correlation
coefficient
calculation and interpretation of relationship between variables Demonstration of basic knowledge on
correlation
coefficient
calculation and interpretation of relationship between variables Demonstration of poor knowledge on correlation coefficient calculation and interpretation of relationship between
variables
HI6007 STATISTICS FOR BUSINESS DECISIONS
c. Estimating regression equation and interpreting slope and intercept coefficient Demonstration of outstanding knowledge on regression model estimation and
interpretation Demonstration of very good knowledge on regression model estimation and
interpretation Demonstration of good knowledge on regression model estimation and
interpretation Demonstration of basic knowledge on regression model estimation and
interpretation Demonstration of poor knowledge on regression model estimation and
interpretation
d.Estimating coefficient of determination and interpreting values.
Demonstration of outstanding knowledge on coefficient of determination calculation and interpretation of relationship between variables Demonstration of very good knowledge on coefficient of determination calculation and interpretation of relationship between variables Demonstration of good knowledge on coefficient of determination calculation and interpretation of relationship between variables Demonstration of basic knowledge on coefficient of determination calculation and interpretation of relationship between variables Demonstration of poor knowledge on coefficient of determination calculation and interpretation of relationship between variables
e. Testing the significance of the relationship between
Dependent and independent variable of the model. Demonstration of outstanding knowledge on model significance Demonstration of very good knowledge on model significance Demonstration of good knowledge on model significance Demonstration of basic knowledge on model significance Demonstration of poor knowledge on model
significance
f. Calculating standard error of the model and commenting on fitness of the regression model. Demonstration of outstanding knowledge on standard error calculation and model fitness estimation. Demonstration of very good knowledge on standard error calculation and model fitness estimation. Demonstration of good knowledge on standard error calculation and model fitness estimation. Demonstration of basic knowledge on standard error calculation and model fitness estimation. Demonstration of poor knowledge on standard error calculation and model fitness estimation.
HI6007 STATISTICS FOR BUSINESS DECISIONS
Academic Integrity
Holmes Institute is committed to ensuring and upholding Academic Integrity, as Academic Integrity is integral to maintaining academic quality and the reputation of Holmes’ graduates. Accordingly, all assessment tasks need to comply with academic integrity guidelines. Table 1 identifies the six categories of Academic Integrity breaches. If you have any questions about Academic Integrity issues related to your assessment tasks, please consult your lecturer or tutor for relevant referencing guidelines and support resources. Many of these resources can also be found through the Study Sills link on Blackboard.
Academic Integrity breaches are a serious offence punishable by penalties that may range from deduction of marks, failure of the assessment task or unit involved, suspension of course enrolment, or cancellation of course enrolment.
Table 1: Six categories of Academic Integrity breaches
Plagiarism Reproducing the work of someone else without attribution. When a student submits their own work on multiple occasions this is known as self-plagiarism.
Collusion Working with one or more other individuals to complete an assignment, in a way that is not authorised.
Copying Reproducing and submitting the work of another student, with or without their knowledge. If a student fails to take reasonable precautions to prevent their own original work from being copied, this may also be considered an offence.
Impersonation Falsely presenting oneself, or engaging someone else to present as oneself, in an in-person examination.
Contract cheating Contracting a third party to complete an assessment task, generally in exchange for money or other manner of payment.
Data fabrication and falsification Manipulating or inventing data with the intent of supporting false conclusions, including manipulating images.
Source: INQAAHE, 2020
HI6007 STATISTICS FOR BUSINESS DECISIONS

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