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,...

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.