Consider the ‘Diamond’ dataset. It is provided as Microsoft Excel Workbook. Data are for 308 round-cut diamonds, taken from a newspaper ad. The codes are as follows:

1. IDNO – an identification number for each data point.

2. WEIGHT – weight of the diamond, in carats.

3. COLOR – degree of colour purity in the diamond: “D” represents top colour purity grade; Lesser grades are “E”, “F”, and so on, through the alphabet. Note that these grades are ranked and should be coded according to their order.

4. CLARITY – diamond clarity (presence or absence of minute flaws): “IF” means “internally flawless” – the top grade; “VVS1” and “VVS2” for “very very slightly imperfect”; and, “VS1” and “VS2” for “very slightly imperfect”. Note that these grades are ranked and should be coded according to their order.

5. RATER – the diamond was evaluated by one of three independent rating agencies: “GIA” – Gemmological Institute of America (based in New York); “IGI” -International Gemmological Institute (Antwerp); “HRD” – Hoge Raad Voor Diamant (Antwerp). 6. PRICE – in Singapore dollars.

2. Question 2:

2.1. One of the claims in the diamond market is that the rating agencies have influence (or are contingent on) the rating of diamonds according to their clarity (presence or absence of minute flow). You are required to conduct the relevant statistical technique to prove or disprove that claim.

2.2. In the diamond market, it is always argued that diamond price is associated with diamond weight. Provide statistical evidence to prove or disprove that argument.

3. Question 3 3.1. Another argument in the diamond market is that diamond price is predicted by diamond weight. Provide statistical evidence to prove or disprove that argument.

3.2. A fourth argument is that diamond weight is not the sole predictor of diamond price. Diamond color and clarity are also significant predictors or diamond price. Provide statistical evidence to prove or disprove that argument.