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: IDNO - an identification number for each data point. WEIGHT - weight of the diamond, in carats. 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. 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. 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). PRICE - in Singapore dollars.   PLEASE REFER TO THIS LINK ALL DATA INSIDE THIS https://files.fm/u/ef7g9uvqx     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? Please how to using  relevant SPSS output (graphs, tables)Please interpret, discuss and answer with screenshot and should proceed if any of the assumptions is violated. However, how to write ?. Please explain.     3.2. A fourth argument is that diamond weight is not the sole predictor of diamond price. Diamond colour and clarity are also significant predictor or diamond price. Provide statistical evidence to prove or disprove that argument? Please how to using  relevant SPSS output (graphs, tables)Please interpret, discuss and answer with screenshot and should proceed if any of the assumptions is violated. However, how to write ?. Please explain.