1. Outliers are usually eliminated from data analysis. However, there are cases where you would not want to eliminate outliers. Discuss why you would want to keep outliers in your analysis. Give 2 examples from the industry in which you work. e.g. Fraudulent transactions detection in banking.

2. If for a certain continuous variable (measure) the 25th percentile value is 800 and the 75th percentile value is 1300, what values could be considered outliers based on the rule of thumb 1.5 * IQR?

3. As part of a data analytics project, Wollongong city council wants utilities consumption data analysed at postcode level. They are interested in calculating the ratio between per capita water use and per capita electricity use. If the structure of the data table they use is given below, write a formula to calculate this new measure. In your formula, just use the variable/column names. Postcode total_water_usage (mL) total_electricity_usage (kwh) population 2500 150 96 102000 .... .... .... ....

4. If your company wants you to visualise average age of customers (measure), their monthly income (measure), distance from the nearest retail store to home (measure) and the industry sector in which they work (category), what kind of chart would you use. Explain and sketch.