3. THE POWER OF Diamonds We thirst for diamonds because we
believe them to be rare and because they are perceived by others to
have a certain power power from wealth, power from love, power from
crackling sexuality, power from kinship with all of the above. The
belief in a diamonds power is its power. (Tom Zoellner )
4. THE IMPORTANCE OF Diamonds Social: Marriage, Feminism,
Class, Values Economic: Globalization, Investment, Market,
Advertisement... Political: Colonialism, Environmental, Conflict
& War (Blood Diamond)
5. The 4 Cs Carat Color Clarity Cut factors for pricing a
Diamond
6. The Classic 4 Cs Carat: Weight of the diamond. One carat
equal to 200 milligrams. Color: Based on absence of color. D-E-F
represent colorless. Clarity: Measures internal characteristics of
stone, referred to as inclusions and blemishes. Cut: Not the design
(round, emerald, etc.) but how the facets of the stone interact
with light, which is the sparkle factor. factors for pricing a
Diamond
7. Our 4 Cs (substituting certification for cut) Carat: Weight
of the diamond. One carat equal to 200 milligrams. Color: Based on
absence of color. D-E-F represent colorless. Clarity: Measures
internal characteristics of stone, referred to as inclusions and
blemishes. Certification: Evaluation by a gemologist grading the
diamond according to the 4 Cs. factors for pricing a Diamond
8. Which Diamond Costs More?
9. Data File: DIAMONDS (1st ten observations)
10. Scatter Plot Matrix VISUAL (relation among all variables)
Non-linearity between price and carat No interaction among other
predictors
11. Correlation Matrix STATISTICAL (relation among all
variables) Strong correlation: price * carat No interaction among
other predictors
12. Partial Plots VISUAL (inspection of residuals) Price *
Predictors (intercept, carat, colors D, E, F, G) All plots indicate
linear relation
14. Partial Plots (continued) Predictors * Price
(certification: IGI) Plot does not indicate linear relation
exists
15. Scatter Plot VISUAL relation between (price * carat)
Non-linearity issue - see stacked data on Carat axis between 1.0
and 1.1 Large concentration of pricing on lower and higher
ends
16. Normal Plot VISUAL relation between (price * carat)
Consistent with scatter plot Issue with the normality
assumption
17. Residual Plot Initial Model All Variables VISUAL
(inspection) Non-linearity issue (curvilinear clearly reflected in
plot) Issue with the constant variance assumption
18. Normal Plot Initial Model All Variables VISUAL (inspection)
Inconsistent data with the expected line - low to high Issue with
the normality assumption
19. Residual Plot Transformation All Variables (log Price)
VISUAL (inspection) Non-linearity issue (curvilinear clearly
reflected in plot) Issue with the constant variance assumption
20. Normal Plot Transformation All Variables (log Price) VISUAL
(inspection) Improvement but more correction needed Issue with the
normality assumptionstill
21. Residual Plot Difference (Carat Diff + Square) VISUAL
(inspection) Regression is linear Constant Variance assumption
satisfied
22. Further improvement but close enough? Assumption of
normality satisfied Normal Plot Difference (Carat Diff + Square)
VISUAL (inspection)
23. BEST *5* MODELS (GIA removed from highlighted number one
choice)