59718 – HOLMES INSTITUTEFACULTY OFHIGHER EDUCATIONAssessment

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OFHIGHER EDUCATIONAssessment Details and Submission GuidelinesTrimester T2 2020Unit Code HI6007Unit Title Statistics for Business DecisionsAssessment Type Assessment 2Assessment Title AssignmentPurpose of the assessment (with ULO Mapping) Students are required to show understanding of the principles and techniques of business and statistical analysis taught in the course.Weight 40% of the total assessmentsTotal 40Word limit N/ADue Date Week 10SubmissionGuidelines • 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 , and listed appropriately at the end in a reference list using Harvard referencing style.HI6007 STATISTICS FOR BUSINESS DECISIONS GROUP ASSIGNMENTAssignment SpecificationsPurpose: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 problems2. Identify and evaluate valid statistical techniques in a given scenario to solve business problems3. Explain and justify the results of a statistical analysis in the context of critical reasoning for a business problem solving4. Apply statistical knowledge to summarize data graphically and statistically, either manually or via a computer package5. Justify and interpret statistical/analytical scenarios that best fit business solutionAssignment 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 .• 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 )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 )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 )Question 2 (10 )ABZ research consultancy firm conducted a of how chief executive officers (CEOs) spend their day. The 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 1519 20 13 15 2323 21 15 20 2116 15 18 18 1919 22 23 21 12a. Prepare a numerical summary report including the summary measures, mean, median, range, variance, standard deviation, and coefficient of variation, smallest and largest values, and thethree 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, Melbourned. 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 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 18Overweight (Kg) 8 3 0 0 6 6 3 3 -4 4 4 2 1 6 -3a. 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) betweenTelevision(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 . (3 marks)f. What is the value of the standard error of the estimate (se). Then, comment on the fitness of thelinear 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 criteriaMarking criteria WeightingQuestion 1a. Understanding the data type and scale of measurementsb. Appropriate graphical technique to present the survey results and review of the summarized data. 5 marks2 marks3 marksQuestion 2a. Understanding descriptive statisticsb. Calculating frequency distributionc. Drawing histogram and analysing the shape of the histogram 10 marks2 marks4 marks4 marksQuestion 3Understanding methods of data collection and method of sampling 10 marksQuestion 4a. Choosing dependent and independent variable correctly and presenting the relationshipb. Calculating correlation and interpreting the valuec. Estimating regression equation and interpreting slope and intercept coefficientd. Estimating coefficient of determination and interpreting values. 15 marks3 marks2 marks3 marks2 markse. 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 marks2 marksTOTAL Weight 40 MarksAssessment Feedback to the Student:Marking RubricExcellent Very Good Good Satisfactory UnsatisfactoryQuestion 1 a. Understanding thedata type and scale of measurementsDemonstration 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 ofmeasurements Demonstration of basic knowledge on data types and scale of measurements Demonstration of poor knowledge on data types and scale of measurementsb. Appropriate graphicaltechnique to present the survey results and review of the summarized data. Demonstration of outstanding knowledge on graphical techniques and critical review of summarised dataDemonstration of very good knowledge on graphical techniques and critical review ofsummarised dataDemonstration ofgood knowledge graphical techniques and critical review ofsummarised dataDemonstration of basic knowledge ongraphical techniques and critical review ofsummarised dataDemonstration of poor knowledge on graphical techniques and critical review of summarised dataQuestion 2 a. Understandingdescriptive statistics Demonstration of outstanding knowledge on descriptive measuresDemonstration of very good knowledge on descriptive measures Demonstration of good knowledge on descriptive measuresDemonstration of basic knowledge on descriptive measuresDemonstration of poor knowledge on descriptive measuresb Calculating frequencydistribution.Demonstration of outstanding knowledge on frequency calculation. Demonstration of very good knowledge onfrequency calculation.Demonstration of good knowledge on frequency calculation. Demonstration of basic knowledge on frequency calculation.Demonstration of poor knowledge on frequency calculation.c. Drawing histogramand analysing the shapeof the histogramDemonstration 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 histogramQuestion 3 Understanding methods of data collection and method of sampling Demonstration of outstanding knowledge on methods of data collection andmethod of sampling Demonstration of very good knowledge on methods of data collection and method ofsampling Demonstration of good knowledge on methods of data collection and method ofsampling Demonstration of basic knowledge on methods of data collection and method ofsampling Demonstration of poor knowledge on methods of data collection and method of samplingQuestion 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 valueDemonstration of outstanding knowledge on correlation coefficient calculation and interpretation of relationship betweenvariables Demonstration of very good knowledge oncorrelationcoefficientcalculation and interpretation of relationship between variables Demonstration of good knowledge oncorrelationcoefficientcalculation and interpretation of relationship between variables Demonstration of basic knowledge oncorrelationcoefficientcalculation and interpretation of relationship between variables Demonstration of poor knowledge on correlation coefficient calculation and interpretation of relationship betweenvariablesHI6007 STATISTICS FOR BUSINESS DECISIONSc. Estimating regression equation and interpreting slope and intercept coefficient Demonstration of outstanding knowledge on regression model estimation andinterpretation Demonstration of very good knowledge on regression model estimation andinterpretation Demonstration of good knowledge on regression model estimation andinterpretation Demonstration of basic knowledge on regression model estimation andinterpretation Demonstration of poor knowledge on regression model estimation andinterpretationd.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 variablese. Testing the significance of the relationship betweenDependent 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 modelsignificancef. 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 DECISIONSAcademic IntegrityHolmes 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 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 breachesPlagiarism 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, 2020HI6007 STATISTICS FOR BUSINESS DECISIONS

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