I need a no mistake assessment pls do it with full focus pls
BUS708 Statistics and Data Analysis Inferential Statistics Report Assignment 2 (Assessment 4) – Individual Word Report – Trimester 1, 2020 1 OVERVIEW OF THE ASSIGNMENT This assignment will test your skill to present and summarise data as well as to make basic statistical inferences in a business context. You will use the results and any feedback given in Assignment 1 (Assessment 3, Excel Report) and produce a single report in a word document. You will need to construct interval estimates, perform suitable hypothesis tests and regression analysis and make conclusion and suggestion for management action. Your report should be written in a word document and should be submitted to Turnitin following the requirement explained below. 2 TASK DESCRIPTION There are two datasets involved in this assignment: Dataset 1 and Dataset 2, which are the same datasets used in Assignment 1 (Excel Report). All data processing should be performed in Excel or Statkey (http://www.lock5stat.com/StatKey). Specific instruction as to which tools should be used for each section will be given during tutorials. Your tasks are to answer the following research questions given in Section 2 to Section 6 below using dataset 1 or dataset 2 as indicated in each section. To answer each question, you will need to first present the relevant numerical summary (summary statistics) and graphical display and perform suitable statistical analysis to provide a conclusion. Your tasks are described below. 1. Section 1: Introduction Provide a brief and clear introduction about the report (e.g. the objective of the report, the datasets involved, etc.). Find relevant articles (minimum one article, maximum 3 articles) and write a proper literature review which includes in-text citation. 2. Section 2: Is Flat makes up about 50% of Dwelling Type? Using Dataset 1, first provide both numerical summary as well as graphical display that easily shows the proportions of dwelling type. Then construct a 95% confidence interval of the population proportion of dwelling type. Finally, answer the research question using the confidence interval. http://www.lock5stat.com/StatKey 3. Section 3: Is the Average Weekly Rent of Flats in Sydney More than $800? Using Dataset 1, first describe the weekly rent distribution of Flats in Sydney (postcode 2000). You need to provide numerical summary (sample size, mean, standard deviation and median) as well as graphical display which shows any outliers. Then perform a suitable hypothesis test to answer the research question above at 5% level of significance. 4. Section 4: Is there a difference in Weekly Rent among five different postcodes? Using Dataset 1, describe the distribution of Weekly Rent for each of the following postcodes: 2000 (Sydney), 2017 (Waterloo), 2145 (Westmead), 2150 (Parramatta), and 2170 (Liverpool). You need to provide both numerical summary as well as graphical display which shows any outliers. Then perform a suitable hypothesis test to answer the research question above. Use a 5% significance level. 5. Section 5: Can we predict the Weekly Rent for flats in Sydney using the Number of Bedrooms? Using Dataset 1, first describe the relationship between the weekly rent and the number of bedrooms for flats in Sydney. You need to provide both numerical summary as well as graphical display. Then interpret the correlation coefficient, coefficient determination and the relevant p-values and use them to answer the research question. 6. Section 6: Is there any relationship between country of origin and suburb where international students live? Using Dataset 2, describe the relationship between the country of origin of an international student and the suburb they currently live in. You need to provide both numerical summary and graphical display. Then perform a suitable hypothesis test to answer the research question above. Use a 5% significance level. 7. Section 7: Conclusion Write a summary of all the findings in the previous sections and then write concluding statements that would benefit a stake holder (e.g. an investor or a renter) to take management action. Finally, suggest further research by discussing an interesting topic or a research question that can be further explored related to the datasets. 3 SUBMISSION REQUIREMENT Deadline to submit the report: Monday, 1st June 2020, 23:59 (11:59pm) You need to submit a word document file to Turnitin which shows all computer outputs and discussion. You do not need to submit the dataset. 4 MARKING CRITERIA Students are advised to read the marking rubric provided on Moodle as well as detailed marking criteria based on this rubric. 5 DEDUCTION, LATE SUBMISSION AND EXTENSION Late submission penalty: - 5% of the total available marks per calendar day unless an extension is approved. This means 0.75 marks (out of 15 marks) per day. For extension application procedure, please refer to Section 3.3 of the Subject Outline. Please do NOT email the lecturer or tutor to seek an extension, you need to follow the procedure described in the Subject Outline. 6 PLAGIARISM Please read Section 3.4 Plagiarism and Referencing, from the Subject Outline. Below is part of the statement: “Students plagiarising run the risk of severe penalties ranging from a reduction through to 0 marks for a first offence for a single assessment task, to exclusion from KOI in the most serious repeat cases. Exclusion has serious visa implications.” “Authorship is also an issue under Plagiarism – KOI expects students to submit their own original work in both assessment and exams, or the original work of their group in the case of a group project. All students agree to a statement of authorship when submitting assessments online via Moodle, stating that the work submitted is their own original work. The following are examples of academic misconduct and can attract severe penalties: • Handing in work created by someone else (without acknowledgement), whether copied from another student, written by someone else, or from any published or electronic source, is fraud, and falls under the general Plagiarism guidelines. • Students who willingly allow another student to copy their work in any assessment may be considered to assisting in copying/cheating, and similar penalties may be applied. ” Section 1
DATA SET 1 DESCRIPTION The data is a secodary data since it is obtained from an already existing source. It was collected from Fair Trading Website (https://www.fairtrading.nsw.gov.au/about-fair-trading/data-and-statistics/rental-bond-data) and it is a subset of "Rental bond lodgement data 2019." The data has got four variables both categorical and numeric. Postal code and number of bedrooms are categorical varibles of the nominal type since values in them are labels with no significant value. They indicate the postal code of the suburb and number of bedrooms in a dwelling type respectively. Dwelling type is a categorical variable indicating the type of housing and lastly weekly rent is a numeric variable of ratio type (Hinton, 2014). It represent the weekly rent paid for any type of dwelling. DATA SET 2 DESCRIPTION The data is primary data since it was collected directly from an online survey. It was collected by radomly surveying responses from international students in an online poll regarding the suburbs where they dwell. Only 30 responses were randomly picked. The sample has got four variables all of which are categorical. The variables are gender indicating whether the individual was male or female, origin indicating the country of origin, postal code and suburb indicating the place where the student resided. Despite the sample being sufficiently large for statistical analysis, there was a possibility of bias, this is because the online poll had no way to check who participated and it would be easy for a local to impersonate an international and participate in the poll (Freund, 2014). Section 2 Dwelling TypePart 1: Summary Statistics and Pie Chart FlatDwelling TypeFrequencyProportion HouseFlat504150% FlatHouse375138% FlatOthers3363% HouseTerrace6777% FlatUnknown1952% TerraceTotal10000100% Terrace House Flat Flat House FlatPart 2 FlatHypothesis Test House HouseData FlatNull Hypothesis p =0.5 FlatLevel of Significance0.05 FlatNumber of Items of Interest5041 HouseSample Size10000 Flat HouseIntermediate Calculations FlatSample Proportion0.5041 FlatStandard Error0.0050 UnknownZ Test Statistic0.8200 Flat UnknownUpper-Tail Test FlatUpper Critical Value1.6449 Flatp-Value0.2061 TerraceDo not reject the null hypothesis Flat House House Flat Flat Flat Flat Flat House Flat Terrace House Flat Terrace Flat Flat Flat House Flat Flat Flat Flat Flat Others Flat House Flat House Flat House Flat Terrace Flat Flat House House House Flat Flat House Flat House Flat Flat Flat House Terrace Flat Flat Others House Flat House House House House Flat Flat House House Flat Flat Flat Flat Flat Unknown Flat Flat House Flat House Flat Flat Terrace Flat House Flat Others House Flat House House Flat House Terrace House House Unknown Flat Flat Flat Flat Flat House House Flat Terrace Others Flat Unknown Terrace House Flat Flat Flat Flat Flat Flat House Flat Flat House Flat House Flat Flat House Flat Flat House House House House Flat Flat House House House Flat House Terrace Flat Flat Flat House House House Flat House Flat House Flat House Flat House House Flat House House Flat House House House House Flat House House House Flat Flat Flat House House Flat Others House Flat Unknown House Flat House Flat House House House House Terrace House House Flat Flat Flat Flat Flat House Flat House Flat Flat House House House Flat Flat House House Flat Flat Flat House Flat Flat Flat Terrace Flat Flat Flat House Flat Others Flat House House Others House Flat Others House House Flat Flat Flat Flat Flat Flat Flat House House Flat Flat House House Others Terrace House Flat House House House Flat House Flat House Flat Flat House House Unknown Flat Terrace House House House Flat House Terrace House Flat House House Flat Flat House House Flat House House Flat House House Flat Flat House Flat Flat House Flat House Flat Flat Flat House Flat Flat Flat House Flat House Flat Others Flat House House Others Flat House Unknown Flat Flat Flat Unknown Flat House Flat House Unknown House Flat House House Flat House Flat Flat House Flat House House Flat House Flat House Terrace House Flat Flat Flat Flat House Flat Flat House Flat Flat Flat House House Flat Flat House House Flat House House Flat Terrace Flat House Flat Flat Terrace House House House Flat Flat Terrace Flat House House House House Flat Terrace House Flat House House Flat House Flat Flat Terrace Flat House Flat Flat Flat Flat Flat Flat Flat Flat Terrace Flat House House House House House House Flat House House Flat House House Flat House House House Flat House House House House House House Terrace Flat Flat Flat Flat House House Flat Flat House Flat House House House Terrace Flat House House House Flat Flat House Flat Others House House Flat House