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.

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. Note: Your study should be based on one of the following sectors/ markets and you should be able to provide a reasonable justification for your selection. a. Australian Stock Exchange b. Australian Real Estate Market c. Travel and Tourism Sector (Please attend weekly interactive tutorials to get more information. You are required to get the approval via email prior the commencement of your study). 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 must use most recent data, share the details on data sources and not allowed to use hypothetical data. 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 and Interpret the meaning of all the coefficients. b. Interpret the coefficient of determination. c. At 5% significance level, test the overall model significance. d. At 5% significance level, assess the significance of independent variables in the model. e. Based on the above analysis in a -d, discuss the usefulness of the model for prediction purposes.