Part 1: Description of Assignment This exercise develops a multi-variable equation that estimates the market price of diamonds based on the “4Cs” of diamond valuation: Carat, Color, Clarity, Cut.Use the attached data file ABOVE. The data is from a very old edition of the Diamond Buyer’s Guide. [Don’t do it for this exercise, but one can adjust for inflation by (1 average inflation rate)^(number of compounding years) ] Discussion and Suggested ApproachWe have numeric (ratio) and non-numeric (categorical) data appearing in the first worksheet of the attached workbook. Tables are provided to code the non-numeric data (substitute a number for a categorical variable) on the second worksheet.On the third worksheet, a blank coding table is provided with the first row already coded properly using the “vlookup” function.You may code this worksheet manually OR let Excel perform the “heavy lifting” by using the “vlookup” function*. Toggle it into formula view to function and its syntax. Note that the numeric data is referenced directly. *CAUTION: Understand this function! It is introduced again in Chapter 10. Development and InterpretationConfigure the regression tool to include the “4C’s.INPUT Options: Perform a regression and inculde the column labels from the data table. OUPUT Options: Set the ouput on a New Worksheet Ply named Regression Specify all residual options and the normal probability plot. Using Yellow highlight, highlight the cells that you referenced in your regression report to answer the questions below.[A] Linear Equation — Develop a regression equation using the data provided based on the “4Cs” and estimate the market price of a diamond based on the parameters below (1 points 2 for follow-on Q): Weight = 1.5 CaratsColor Grade: EClarity Grade: VS1Cut Category: Good Use Excel to perform the above sumproduct computation. [B] Fit — Determine the error and what proportion of the total variation in the market price of diamonds that the model’s selected regression variables explain. (2 points) [C] Statistical Significance — Determine which regression variables are statistically significant and determine which one (parameter) you would eliminate, if any, if it was necessary to further refine the model. (3 points)[D] Residual Analysis — Determine which sample observation represents the best value from the buyer’s perspective and the largest windfall from the seller’s perspective (when sold). (2 points) [E] Statistical Inference –Detemine if the model developed from this set of sample data could be applied to the general population. Part 2: Once you’ve developed your model based on the instructions in Step 1, validate your results and your interpretations by answering the questions.
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