Recent Question/Assignment

HOLMES INSTITUTE
FACULTY OF
HIGHER EDUCATION
Assessment Details and Submission Guidelines
Trimester T1 2021
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 (30th of May 2021)
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 subject to penalties.
Group Assignment Questions
Assume your group is the team of data analytics in a renowned Australian company. The company offers their assistance to distinct group of clients including (not limited to), public listed companies, small businesses, educational institutions etc. Company has undertaken several data analysis projects and all the projects are based on multiple regression analysis.
Based on the above assumption, you are required to.
1. Develop a research question which can be addressed through multiple regression analysis. 2. Explain the target population and the expected sample size 3. Briefly describe the most appropriate sampling method.
4. Create a data set (in excel) which satisfy the following conditions. (You are required to upload the data file separately).
a. Minimum no of independent variables – 2 variables
b. Minimum no of observations – 30 observations
Note: You are required to provide information on whether you used primary or secondary data, data collection source etc.
5. Perform descriptive statistical analysis and prepare a table with following descriptive measures for all the variables in your data set.
Mean, median, mode, variance, standard deviation, skewness, kurtosis, coefficient of variation.
6. Briefly comment on the descriptive statistics in the part (5) and explain the nature of the distribution of those variables.
7. Derive suitable graph to represent the relationship between dependent variable and each independent variable in your data set.
(ex: relationship between Y and X1, Y and X2 etc)
8. Based on the data set, perform a regression analysis and correlation analysis, and answer the questions given below.
a. Derive the multiple regression equation.
b. Interpret the meaning of all the coefficients in the regression equation.
c. Interpret the calculated coefficient of determination.
d. At 5% significance level, test the overall model significance.
e. At 5% significance level, assess the significance of independent variables in the model.
f. Based on the correlation coefficients in the correlation output, assess the correlation between explanatory variables and check the possibility of multicollinearity.
Marking criteria
Marking criteria Weighting
Developing a research question which can be addressed through multiple regression analysis. 5 marks
Explaining the target population and the expected sample size
4 marks
Describing the most appropriate sampling method.
4 marks
Performing descriptive statistical analysis and review of the calculated values 4 marks
Deriving suitable graph to represent the relationship between dependent variable and each independent variable in your data set. 4 marks
Deriving multiple regression equation based on the regression output and interpretation of the regression coefficients
6 marks
Interpreting the calculated coefficient of determination.
2 marks
Assessing the overall model significance.
4 marks
Assessing the significance of independent variables in the model.
3 marks
Examining the correlation between explanatory variables and check the possibility of multicollinearity.
4 marks
TOTAL Weight 40 Marks
Assessment Feedback to the Student:
Marking Rubric
Excellent Very Good Good Satisfactory Unsatisfactory
Developing a research question which can be addressed through
multiple regression analysis. Demonstration of outstanding knowledge on research question which can be solved with regression analysis. Demonstration of very good knowledge research question which can be solved with regression analysis. Demonstration of good knowledge on research question which can be solved with regression analysis. Demonstration of basic knowledge on research question which can be solved with regression analysis. Demonstration of poor knowledge on research question which can be solved with regression analysis.
Explaining the target population and the
expected sample size
Demonstration of outstanding knowledge on identifying target population for a research and a suitable sample size.
Demonstration of very good
knowledge on identifying target population for a research and a suitable sample size.
Demonstration of good knowledge on identifying target population for a research and a suitable sample size.
Demonstration of basic knowledge on identifying target population for a research and a suitable sample size.
Demonstration of poor knowledge on identifying target population for a research and a suitable sample size.
Describing the most appropriate sampling method.
Demonstration of outstanding knowledge on random and nonrandom sampling methods and selection of the best sampling method for a given case. Demonstration of very good knowledge random and non-random sampling methods and selection of the best sampling method for a given case. Demonstration of good knowledge on random and non-random sampling methods and selection of the best sampling method for a given case. Demonstration of basic knowledge on random and nonrandom sampling methods and selection of the best sampling method for a given case. Demonstration of poor knowledge on random and non-random sampling methods and selection of the best sampling method for a given case.
Performing descriptive statistical analysis and
review of the calculated
values 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
Deriving suitable graph to represent the relationship between variables Demonstration of outstanding knowledge on presentation of data using suitable chart types. Demonstration of very good
knowledge on presentation of data using presentation of data using suitable chart types. Demonstration of good knowledge on presentation of data using suitable chart types. Demonstration of basic knowledge on presentation of data using suitable chart types. Demonstration of poor
knowledge on presentation of
data using suitable chart types.
Deriving multiple regression equation based
on the regression output.
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
Interpreting the calculated coefficient of
determination.
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
Assessing the overall model significance.
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
Assessing the significance of independent variables in the model.
Demonstration of outstanding knowledge on significance of independent variables. Demonstration very good
knowledge
significance independent variables. of
on of Demonstration of good knowledge on significance of independent variables. Demonstration of basic knowledge on significance of independent variables. Demonstration of poor knowledge on significance of independent variables.
Examining the correlation between explanatory
variables and check the possibility of
multicollinearity.
Demonstration of outstanding knowledge on correlation coefficient calculation, interpretation of relationship between variables and assessing multicollinearity. Demonstration very good knowledge on correlation coefficient calculation, interpretation relationship between
variables and assessing multicollinearity. of
of
Demonstration of good knowledge correlation coefficient calculation, interpretation of
relationship
between variables and assessing multicollinearity. Demonstration of basic knowledge on correlation coefficient calculation, interpretation of
relationship between
variables and assessing multicollinearity. Demonstration of poor knowledge on correlation coefficient calculation, interpretation of relationship between variables and assessing multicollinearity.
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

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