Diamond Price Determinants Analysis
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D
IAMOND
A
NALYSIS
Zachary Baine
Comm-486
Project Introduction
•
For the following project, I have created a brief, hypothetical statistical
analysis presentation. The presentation is based off a sample set of 310
diamonds that I have analyzed using JMP statistical software. The data
includes 5 variables for each diamond : the diamond’s cut, color, clarity,
carat, and price. Each of these variables, excluding price, has its own
rating system. The purpose of this presentation is to explain how each of
these variables determines the price of a diamond. In a real world
application this information could be used to inform a company, investor,
etc. how diamonds should be priced in a competitive market.
•
As this is a short presentation I will not delve too far into the
mathematics behind a diamond’s pricing, but rather explain what goes
into a diamond’s price and short explanation why. That being said, let’s
get started.
After conducting my
analysis, I have found a
few key factors that
really seem to
determine the price of
each diamond. For
starters, which of the
four c’s (cut, color,
clarity, and carat) do
you think impacts a
diamond’s price the
most?
(click on the diamond
to select your choice)
CUT
CLARITY
COLOR
CARAT
Unfortunately not. A
diamond’s cut is not the
most impactful variable
on a diamond’s price.
Click on continue to find
the most impactful or
retry to guess again.
CONTINUE
Unfortunately not. A
diamond’s color is not
the most impactful
variable on a diamond’s
price. Click on continue
to find the most
impactful or retry to
guess again.
Unfortunately not. A
diamond’s clarity is not
the most impactful
variable on a diamond’s
price. Click on continue
to find the most impactful
or retry to guess again.
RETRY
That’s right, a diamond’s carat increases the price of it
more than any other variable by far. How do we know
this? After formatting the data, I ran a correlation
analysis which yields the following table:
Looking at the corresponding values between the
column LnPrice and the variables LnCarat, Dum Cut,
Dum Color, and Dum Clarity we can see (circled in red)
that the greatest value is between LnCarat and
LnPrice, indicating the most impactful.
How else is price determined?
•
Statistically speaking it is helpful to know
that the price and carat of a diamond are
strongly related; however, there is more to
the price than just the carat. My next step
was to run multiple regression models in
JMP in order to find out how the other
variables (color, cut, and clarity) impact the
price. To do this I compared price with only
color, cut, and clarity.
A
FTER
RUNNING
THIS
MODEL
,
WHICH
OF
THE
FOLLOWING
DO
YOU
THINK
HAS
THE
MOST
IMPACT
ON
A
DIAMOND
’
S
PRICE
?
Unfortunately not. A
diamond’s cut is not the most
impactful remaining variable.
Click on continue to find the
most impactful remaining
variable or retry to guess
again.
•
Unfortunately not. A diamond’s cut is not
the most impactful remaining variable. Click
on continue to find the most impactful
remaining variable or retry to guess again.
As it turns out, it is clarity that is the
second most impactful variable. In
order to avoid being too technical, let
me just say that we can determine
this since the coefficients yielded by
the model were much greater for
clarity than for cut or color.
Conclusion
From all of this we can now determine that
the price placed on a diamond is mainly
based on its carat and then by its clarity. As
a matter of fact, over 90% of the variability
in a diamond’s price can be determined by
these two characteristics alone; it turns out
the cut and color of the diamond have little
to no impact on its price.
This project presents a statistical analysis based on a sample set of 310 diamonds, examining how variables such as cut, color, clarity, carat, and price impact diamond pricing. Through correlation analysis, it revealed that carat has the most significant effect on diamond prices. The presentation focuses on explaining the factors influencing diamond pricing, providing insights valuable for companies and investors in pricing diamonds competitively.
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Presentation Transcript
DIAMOND ANALYSIS Zachary Baine Comm-486
Project Introduction For the following project, I have created a brief, hypothetical statistical analysis presentation. The presentation is based off a sample set of 310 diamonds that I have analyzed using JMP statistical software. The data includes 5 variables for each diamond : the diamond s cut, color, clarity, carat, and price. Each of these variables, excluding price, has its own rating system. The purpose of this presentation is to explain how each of these variables determines the price of a diamond. In a real world application this information could be used to inform a company, investor, etc. how diamonds should be priced in a competitive market. As this is a short presentation I will not delve too far into the mathematics behind a diamond s pricing, but rather explain what goes into a diamond s price and short explanation why. That being said, let s get started.
After conducting my analysis, I have found a few key factors that really seem to determine the price of each diamond. For starters, which of the four c s (cut, color, clarity, and carat) do you think impacts a diamond s price the most? (click on the diamond to select your choice) CUT COLOR CLARITY CARAT
Unfortunately not. A diamond s cut is not the most impactful variable on a diamond s price. Click on continue to find the most impactful or retry to guess again. CONTINUE
Unfortunately not. A diamond s color is not the most impactful variable on a diamond s price. Click on continue to find the most impactful or retry to guess again.
Unfortunately not. A diamond s clarity is not the most impactful variable on a diamond s price. Click on continue to find the most impactful or retry to guess again. RETRY
Thats right, a diamonds carat increases the price of it more than any other variable by far. How do we know this? After formatting the data, I ran a correlation analysis which yields the following table: LnPrice LnCarat Dum Cut Dum Color Dum Clarity LnPrice 1.0000 0.9293 0.1124 0.0578 0.2811 LnCarat 0.9293 1.0000 -0.0057 -0.1931 0.0207 Dum Cut 0.1124 -0.0057 1.0000 0.0281 0.2551 Dum Color Dum Clarity 0.0578 -0.1931 0.0281 1.0000 0.0964 0.2811 0.0207 0.2551 0.0964 1.0000 Looking at the corresponding values between the column LnPrice and the variables LnCarat, Dum Cut, Dum Color, and Dum Clarity we can see (circled in red) that the greatest value is between LnCarat and LnPrice, indicating the most impactful.
How else is price determined? Statistically speaking it is helpful to know that the price and carat of a diamond are strongly related; however, there is more to the price than just the carat. My next step was to run multiple regression models in JMP in order to find out how the other variables (color, cut, and clarity) impact the price. To do this I compared price with only color, cut, and clarity.
AFTERRUNNINGTHISMODEL, WHICHOFTHEFOLLOWINGDO YOUTHINKHASTHEMOST IMPACTONADIAMOND S PRICE?
Unfortunately not. A diamond s cut is not the most impactful remaining variable. Click on continue to find the most impactful remaining variable or retry to guess again.
Unfortunately not. A diamonds cut is not the most impactful remaining variable. Click on continue to find the most impactful remaining variable or retry to guess again.
As it turns out, it is clarity that is the second most impactful variable. In order to avoid being too technical, let me just say that we can determine this since the coefficients yielded by the model were much greater for clarity than for cut or color.
Conclusion From all of this we can now determine that the price placed on a diamond is mainly based on its carat and then by its clarity. As a matter of fact, over 90% of the variability in a diamond s price can be determined by these two characteristics alone; it turns out the cut and color of the diamond have little to no impact on its price.
