Overview
A jewelry firm wants to submit a bid to purchase a large collection of diamonds but is uncertain how much it should bid. You will use the results from a predictive model to make a recommendation on how much the jewelry company should bid for the diamonds.
Assignment Details
A diamond distributor has recently decided to exit the market and has put up a collection of diamonds up for auction. Seeing this as a great opportunity to expand its inventory, a jewelry firm is interested in making a bid. To determine how much to bid, the firms analytics department will use a large database of diamond prices to build a multiple regression model to predict the price of a diamond based on its attributes.
As the business analyst, you are tasked to build the regression model and apply that model to make a recommendation for how much the company should bid for the entire collection of diamonds.
Step 1 Research and Reflect:

There have been numerous debates, articles, and even a movie (Blood Diamond) about the mining and international sales of conflict diamonds. Research and present (in no less than 500 words) the nature of the conflict diamond trade and the relevance of Druckers ideas to todays multinational enterprises that participate in such activities.

Step 2 – Understanding the Model:
There are two datasets.

diamonds contains the data used to build the regression model.
new diamonds contains the data for the diamonds the company would like to purchase.

Both datasets contain carat, cut, and clarity data for each diamond. Only the diamonds dataset has prices.

Carat represents the weight of the diamond and is a numerical variable.
Cut represents the quality of the cut of the diamond, and falls into 5 categories: fair, good, very good, ideal, and premium. Each of these categories are represented by a number, 1-5, in the Cut_Ord
Clarity represents the internal purity of the diamond, and falls into 8 categories: I1, SI2, SI1, VS1, VS2, VVS2, VVS1, and IF. Each of these categories are represented by a number, 1-8, in the Clarity_Ord

Using Excel, build the multiple regression model using the diamonds dataset. 

Based on the Summary Output produced by the regression analysis write out the multiple regression model and explain why you are confident (or not confident) in the model to predict the price. NOTE: Copy and paste the summary output into your report.
According to the model, if a diamond is 1 carat heavier than another with the same cut and clarity, how much more would the retail price of the heavier diamond be? Why?
If you were interested in a 1.5 carat diamond with a Very Goodcut (represented by a 3 in the model) and a VS2 clarity rating (represented by a 5 in the model), what retail price would the model predict for the diamond?

Step 3 Calculate the predicted price for each diamond: Using the new diamonds dataset, for each diamond, plug in the values for each of the variables into the model (equation), then solve the equation to get the estimated, or predicted diamond price. 
Step 4 – Visualize the Data: Create two scatter diagrams (or scatter plot).

Plot 1 – Plot the data for the diamonds in the database, with carat on the x-axis and price on the y-axis.
Plot 2 – Plot the data for the diamonds for which you are predicting prices with carat on the x-axis and predicted price on the y-axis.

What strikes you about this comparison?
After seeing this plot, do you feel confident in the models ability to predict prices? Why or why not?

Step 5 – The Recommendation: Now that you have the predicted price for each diamond, its time to calculate the bid price for the whole set. Note: The diamond price that the model predicts represents the final retail price the consumer will pay. The company generally purchases diamonds from distributors at 70% of that price, so your recommended bid price should represent that. What bid do you recommend for the jewelry company? Please explain how you arrived at that number.