Diamonds Regression Lab - Revision history

Carver: /* Step 8: Testing your tool */

2020-01-12T23:34:44Z

‎Step 8: Testing your tool

2020-01-12T23:34:16Z

‎Step 8: Testing your tool

2020-01-12T23:33:05Z

2020-01-12T23:32:29Z

‎Step 7: Your tool in production

2020-01-12T23:30:04Z

‎Step 8: Testing your tool

2020-01-12T23:28:30Z

‎Step 8: Testing your tool

2020-01-12T23:26:58Z

‎Step 8: Testing your tool

2020-01-12T23:26:27Z

‎Step 6: Model interpretation: Slope

2020-01-12T23:07:08Z

‎How can we fix our model so that the intercept is as we expect?

2020-01-12T22:52:58Z

‎Step 6: Model interpretation: Slope

@@ Line 99: / Line 99: @@
 Compute the residual in Step 7, for each diamond in the data set.  Use Data ---> Compute Expression.
-Count the number of diamonds in the data set that would be flagged as possibly measured wrong.  Use Stat ---> Summary Stats.  Choose n (count) as the statistic to be compute, and set a where function based on the residual above to include only those flagged.  What percentage of diamonds are possibly measured wrong?
+Count the number of diamonds in the data set that would be flagged as possibly measured wrong.  Use Stat ---> Summary Stats.  Choose n (count) as the statistic to be computed, and set a where function based on the residual above to include only those flagged.  What percentage of diamonds are possibly measured wrong?
 == Write up ==
 Write a report that explains what you have done.  Include a short description, with headings for each step above.

@@ Line 97: / Line 97: @@
 === Step 8: Testing your tool ===
-Compute the residual in Step 7, for each diamond in the data set.  Use Data --> Compute Expression.
+Compute the residual in Step 7, for each diamond in the data set.  Use Data ---> Compute Expression.
 Count the number of diamonds in the data set that would be flagged as possibly measured wrong.  Use Stat ---> Summary Stats.  Choose n (count) as the statistic to be compute, and set a where function based on the residual above to include only those flagged.  What percentage of diamonds are possibly measured wrong?

@@ Line 90: / Line 90: @@
   Density = 0.01755 carats/mm^3
   pi = 3.1415
 === Step 7: Your tool in production ===

@@ Line 94: / Line 94: @@
 === Step 7: Your tool in production ===
-Going back to the problem of when to flag a diamond as possibly measured incorrectly, we are going to use your model (with intercept removed).  We are going to measure x, y, z, and carat for the diamond, then compute a residual.  If the residual is too high or too low we are going to flag the diamond.  You choose what residuals are too high and too low based on your understanding of the data.  Based on this choice, write a formula for too high and too low involving x, y, z, b1, and carat.
+Going back to the problem of when to flag a diamond as possibly measured incorrectly, we are going to use your model (with intercept removed).  We are going to measure x, y, z, and carat for the diamond, then compute a residual.  If the residual is too high or too low we are going to flag the diamond.  You choose what residuals are too high and too low based on your understanding of the data.  Based on this choice, write a formula indicating which diamonds are flagged involving x, y, z, b1, and carat, and inequalities.
 === Step 8: Testing your tool ===

@@ Line 100: / Line 100: @@
 Compute the residual in Step 7, for each diamond in the data set.  Use Data --> Compute Expression.
-Count the number of diamonds in the data set that would be flagged as possibly measured wrong.  Use Stat ---> Summary Stats.  Choose n, and set a where function.  What percentage of diamonds are possibly measured wrong?
+Count the number of diamonds in the data set that would be flagged as possibly measured wrong.  Use Stat ---> Summary Stats.  Choose n (count) as the statistic to be compute, and set a where function based on the residual above to include only those flagged.  What percentage of diamonds are possibly measured wrong?
 == Write up ==
 Write a report that explains what you have done.  Include a short description, with headings for each step above.

@@ Line 78: / Line 78: @@
 ==== How can we fix our model so that the intercept is as we expect? ====
-From now on, when you write the model, drop the intercept from the model (set it equal to zero).
+From now on, when you write the model, drop the intercept from the model.  Optional: In StatCrunch there is a way of recalculating the slope under the assumption that the intercept is zero.  It involves choosing "Polynomial" Regression, choosing "Polynomial Order = 1" and checking "Without Intercept".  The answer you get is almost exactly the same, so it's OK to just write the slope term you get with Simple Linear Regression, and don't write down the b0 intercept term.
 === Step 6: Model interpretation: Slope ===

@@ Line 82: / Line 82: @@
 === Step 6: Model interpretation: Slope ===
-What is the slope of your model?  Solve the second equation, below, for "a" and compare the resulting value with the ones for cylinders and right-circular-cones, above:
+What is the slope of your model, b1?  Solve the second equation, below, for "a" and compare the resulting value with the ones for cylinders (a=4) and right-circular-cones (a=12), above:
-  Weight = Slope * x*y*z
+  Weight = b1*x*y*z = Density * Volume_Slope*x*y*z
-  Slope = Density*pi/a
+  Volume_Slope = pi/a
   Density = 0.01755 carats/mm^3